bluuebunny/crossref_metadata_2025_split huggingface dataset:
Point Cloud Data Segmentation Using RANSAC and Localization
Authors: Mohd Isa S. N. , Abdul Shukor S. A. , Rahim N. A. , Maarof I. , Yahya Z. R. , Zakaria A. , Abdullah A. H. , Wong R. 
Published: December 2019

Abstract
In this paper, we present 3D point cloud data segmentation from a Terrestrial Laser Scanner (TLS) using RANSAC and localization approaches. Our work is using the real world data acquired from outdoor and indoor scene of Tawau Bell Tower (also known as the Belfry), one of the heritage building located in Sabah. We adapt the methods to segment ground and nonground data for data reduction. Based on the non-ground information and geometrical constraint, the data managed to show the building structure. The aim is to use and handle a smaller set of data rather than using the entire point cloud data in processing the region of interest. This will also help to reduce processing time as point cloud data from a TLS is usually large in size. The ground plane was fitted with RANSAC algorithm restricted by distance to describe the geometrical of the plane. As a result, the point cloud data was reduced to the environment around Bell Tower and able to highlight the region of interest. Building segmentation is then retrieved from the reference centre location to form a bounding box and data were organized using kd-tree to denote the Bell Tower structure. The framework is tested for outdoor and indoor scan of the Bell Tower and able to segment the data appropriately.
Classification of different materials for underground object using artificial neural network
Authors: Ali H , Ahmad Firdaus A Z , Azalan Mohd Shuhanaz Zanar , Kanafiah S N A M , Salman S H , Ahmad M R , T Amran T Sarah , M Amin M Syafiq 
Published: December 2019

Abstract
Ground Penetrating Radar (GPR) is widely used for non-destructive investigation of the shallow subsurface exploration especially in locating the buried infrastructure such as pipes, cables and road inspections. However, the interpreting hyperbolic signature of buried object in GPR images remains a challenging task. Therefore, this paper presented the classification of different materials based on GPR images using artificial neural network (ANN). In this research, GPR images so called the B-scan radargram represented by hyperbolic signature are firstly acquired and pre-processed. Then, the hyperbolic signature features are extracted using statistical techniques. The extracted features is then fed up as input to the multilayer perceptron (MLP) neural network classifier. A series of experiments have been conducted based on extracted hyperbolic features of different materials and shapes. Based on the results, the proposed method in classifying different materials based on GPR images using neural network showed promising results.
Design and Development of Three-Phase Voltage Source Inverter for Variable Frequency Drive
Authors: Nurhaida A S , Ahmad Firdaus A Z , Kamarudin Kamarulzaman , Azmi S A , Ridzuan M J M , Hafis S M 
Published: December 2019

Abstract
Three phase induction motors are the workhorse of industry. Variable speed operation of the motor enables substantial energy savings through implementation of variable frequency drives. This project concerns on the design and implementation of three-phase voltage source inverter (VSI) for variable frequency drive. The focus was to generate variable frequency output suitable to be fed to the induction motor for the purpose of variable speed control. SPWM signal was generated through SIMULINK algorithm. The VSI circuit was designed, fed with SPWM trigger signal and simulated for variable frequency output in terms of line-to-neutral and line-to-line voltage across R-L load. In the experimental setup, the real VSI system was developed, the SPWM trigger signal generated through SIMULINK and interfaced to hardware using Arduino microcontroller. The output is dropped across resistive load and parameters line-to-neutral and line-to-line voltage was measured. Three set of output voltage with frequency of generated at 20Hz, 50Hz and 70Hz. Thus, experimental result validated that variation of frequency of voltage output of the VSI has been successfully achieved.
Effect of electromagnetic induction spectroscopy of femur bone on electromagnetic signal strength
Authors: Amran M. , Daud R. , Ali Hassan M.K. , Zakaria Z. , Omar M. I. , Mat F. 
Published: December 2019

Abstract
Bone implant is a promising technique to heal the fracture site where it focusing to stabilize and support the bone. Improvement in bone implant are required in order to ensure better healing process because some failures may happen due to the loss of implant stability. Previous studies showed there are lack of investigation on bone implant. Early detection of screw implant loosening is crucial for faster repairing and a healthy ready-to-operate. The purpose of this study is to investigate the signal strength of electromagnetic field induction on human bone by employing a single channel magnetic induction spectroscopy using flat spiral coil sensor. This study also to evaluate the various type of coil sensitivity of the magnetic induction spectroscopy on femur bone. The electromagnetic field induction will be analyse using the COMSOL Multiphysics simulation software to collect the data of received signal strength. The promising experimental results suggested the feasibility of the proposed electromagnetic induction spectroscopy.
Electrochemical Techniques-Based approaches for Mycobacterium Tuberculosis Detection: Last Decade Review
Authors: Alfarhan K , Zakaria A , Yusof N , Zakaria S , Abdullah J , Kamarudin L , Azmi  , Ariffin N 
Published: December 2019

Abstract
Mycobacterium Tuberculosis (MTB) is a common airborne infectious disease that leads to millions of deaths every year worldwide. It is still one of the top ten causes of death and the victims of TB are more than HIV/AIDS in 2017. Traditional approaches for MTB detection are either take a long time, unreliable or high cost. The electrochemical techniques (ECTs) as improved and inexpensive approaches to detect the MTB. Many of ECTs were used in MTB detection such as differential pulse voltammetric (DPV), cyclic voltammetric (CV), square wave voltammetric (SWV), amperometric and impedimetric. Principle of the MTB detection using ECTs depends on DNA hybridization of the MTB on the working electrode of ECTs. The researchers developed biosensors or aptasensors and used them for ECTs analyzing to detect the MTB. They developed various biosensors from various composite and DNA probes but all the developed composite of the biosensors were used to coat the electrodes that used in ECTs. Many types of electrodes and electrolytes were used in MTB detection. The most used ECTs in MTB detection is DPV and CV while the least used is amperometric. The ECTs for MTB detection achieved high sensitivity, reliability, low detection time and very low detection limits.
Metal shape classification of buried object using multilayer perceptron neural network in GPR data
Authors: Kanafiah S N A M , Ali H , Ahmad Firdaus A Z , Zanar Azalan M S , Jusman Y , Khairi A A , Ahmad M R , Sara T , Amran T , Mansor I , Shukor S A A , Cheng E M , Fook C Y 
Published: December 2019

Abstract
Ground penetrating radar (GPR) is a non-destructive tool for subsurface investigation becomes an emerging trend in many applications especially in detecting infrastructure utilities. However, interpreting of GPR data in terms of target’s geometry is remained a challenging task due to the existence intra/inter-class variations and subtle changes of hyperbolic signature pattern in GPR data. To addressed this issue, this paper proposes to classify the basic shape (i.e. cubic, cylindrical and disc) of buried object using multilayer perceptron neural network. The GPR raw data firstly is pre-processed based on A-scan and B- scan GPR data. Then, the combined statistical features with gray level co-occurrence matrix (GLCM) and Hu moment invariants are extracted from A-scan and B-scan. The extracted features are then fed as input to multilayer perceptron to classify the shapes (cubic, cylindrical and disc) of buried object. Based on the experiment conducted, the highest recognition rate has been achieved is about 83.3%. Thus, the proposed method of using combined statistical with GLCM and Hu moment invariants features to classify shape of buried object using multilayer perceptron is promising.
Monitoring Oil Palm Tree Health–A Review
Authors: Hoe J H , Shukor S A A 
Published: December 2019

Abstract
Oil palm is known as one of the most important resources in Malaysia which able to be exported to other countries. Oil palm, also known as “Elaeis guineensis”, has many usages and benefits. To ensure the productivity and quality of the oil palm, the health status of the tree needs to be monitored, as they can be prone to various diseases. This paper is going to discuss current methods other researchers have adapted that able to be used in monitoring oil palm trees health. In addition, some analyses and conclusion were drawn to address related issues in the area.
Gas source localization accuracy: A comparison between conventional, weighted arithmetic mean and kernel-based gas distribution mapping methods in small indoor area
Authors: Yeon A S A , Visvanathan R , Kamarudin K , Mamduh S M , Zakaria A , Kamarudin L M 
Published: December 2019

Abstract
This work focuses on comparing gas source localization accuracy of three types of gas source localization methods which are the conventional method, weighted-averaging mean method and variance map from DM+V method. Gas source localization accuracy in this work means the distance error between the calculated source location and the actual source location. All methods were implemented offline using the measurements collected from real-time gas source localization experiments that were done beforehand. The dataset consists of gas sensor measurements, and mobile robot’s location in a 3m by 6m test area. The goal of the work is to shine a light on why recent works in the field are more focusing on improving probabilistic and map-based algorithms. This work also shows how the kernel size of kernel-based gas distribution mapping might affect the gas source localization accuracy in a small indoor area.
Human Identification through Kinect’s Depth, RGB, and Sound Sensor
Authors: Shunmugam P , Kamarudin K , Mosed A A A , Shukor S A A 
Published: December 2019

Abstract
Human identification is a very important subject in computer field and has been researched widely. This paper proposes human identification system based on Kinect’s individual sensor as well as combination of all its available sensors. In the first part of the project, each sensor on the Kinect (i.e IR depth sensor, RGB camera and microphones) was used for skeleton recognition, face recognition and speech recognition respectively. Then, these individual recognition methods are combined as step-by-step process into a multi-sensor recognition system. Few experiments were carried out to test the reliability of the developed human identification systems. The results show that multi-sensor based human identification system is highly efficient compared to the single-sensor system. This is because, multi-sensor recognition system involves many recognition stages as each recognition stage needs particular biometric information of the user.
A Study of Ultrasonic Sensor Capability in Human Following Robot System
Authors: Tai W. W. , Ilias B. , Abdul Shukor S.A. , Abdul Rahim N. , Markom MA 
Published: December 2019

Abstract
Human following robot is very common in this technology era. Human following is a technique used by robot and autonomous vehicles to follow a human within a specific range. In this case, communication between the human and the robot is the most significant factor where sensor is needed to ensure its successfulness. This paper discussed a human following robot system that utilized ultrasonic sensor. Ultrasonic sensor is preferred for human following robot due to its wide detection area, less light dependency, the ability to detect glass and shining wall, smaller in size, lightweight, use a very low memory, cheaper than Laser Range Finder (LRF) or camera and lower power consumption. However, being a specular type of sensor and not narrowly focused, this can cause wrong estimation and recognition of human, especially at the legs. Therefore, necessary accompanying algorithms should be developed to encounter this issue. Here, three pieces 40 kHz ultrasonic sensors are mounted on the four-wheeled mobile robot platform. Ultrasonic sensors used to detect the human’s leg, and the data collected from the sensors were used upon the design of sensor platform. In order to detect human’s leg, the sensor platform was tested repeatedly to obtain optimum data. The data which have been collected from the ultrasonic sensor will then be interfaced with Arduino software. Thus, this paper outlines a set of benchmarks for human following and briefly evaluates its performances. The human following robot was tested in a real laboratory environment. The results were good, where the implementation of this algorithm is able to produce an accurate decision.
Discovering the Implementation Success Factors for IoT and Big Data Analytics in Transportation System
Authors: Hussein W N , Kamarudin L M , Hussain H N. , Ishak N A , Zakaria A. , Jadaa K J 
Published: December 2019

Abstract
The implementation of intelligent transportation systems, or what is otherwise referred to as IoT and big data analytics in transportation system are affected by several factors. Considering the overall impact of a definite and comprehensive plan that involves business, infrastructure, and organizational administration, improved customers satisfaction can better be achieved when all these antecedent factors are investigated, and their elements are determined. There is no recorded study on multidimensional implementation model where more than one of them is considered. This paper, therefore, stems from the general background of implementation success factors but aims to discover the generality of the definite plan and procedure that must be executed for a successful implementation of big data analytics and IoT-oriented transportation system. This paper employs a systematic literature review and an in-depth interview to address the obvious limitation. This paper also discussed the qualitative approach that was used to gather data on the successful implementation factors in the transportation system. The deliverable from this research will be the implementation success factors for the big data analytics and IoT-oriented transportation system.
Aspirin rechallenge in an adult patient previously diagnosed with Reye syndrome
Authors: Magrum BrookeAnne G , Pickworth Kerry K 
Published: December 2019

AbstractPurposeAspirin has been the cornerstone of antiplatelet therapy in patients with acute coronary syndromes and is well accepted and recommended by several major healthcare organizations. A combination of aspirin and a P2Y12 inhibitor, commonly known as dual antiplatelet therapy, is recommended in patients with coronary stent implantation to reduce the risk of stent thrombosis and ischemic events.SummaryWe recently cared for an adult male who presented with an acute coronary syndrome who had a history of Reye syndrome during childhood. During this admission, he was rechallenged with low-dose aspirin for the first time since his diagnosis of Reye syndrome as a child after aspirin therapy. There have been various case reports in children and adults who have been rechallenged with aspirin within days to weeks after the initial diagnosis of Reye syndrome. These reports show mixed results in children and adults regarding the return of Reye syndrome upon aspirin rechallenge shortly after initial aspirin exposure.ConclusionThis, to our knowledge, appears to be the first report of a low-dose aspirin rechallenge 30 years later in life in an adult patient with a history of Reye syndrome while receiving aspirin therapy during childhood.
eCAMI: simultaneous classification and motif identification for enzyme annotation
Authors: Xu Jing , Zhang Han , Zheng Jinfang , Dovoedo Philippe , Yin Yanbin 
Published: December 2019

Abstract

Motivation
Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction.

Results
This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes.

Availability and implementation
https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI.

Supplementary information
Supplementary data are available at Bioinformatics online.
A local group differences test for subject-level multivariate density neuroimaging outcomes
Authors: Dworkin Jordan D , Linn Kristin A , Solomon Andrew J , Satterthwaite Theodore D , Raznahan Armin , Bakshi Rohit , Shinohara Russell T 
Published: December 2019

SummaryA great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. When the relevant pattern is unknown, summary statistics are often unable to capture differences between disease groups, and their use may encourage post hoc searches for the optimal summary measure. In this study, we introduce the multi-modal density testing (MMDT) framework for the naive discovery of group differences in voxel intensity profiles. MMDT operationalizes multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities of voxel intensities and utilizes kernel density estimation to develop a local two-sample test for individual points within the density space. Through simulations, we show that this method controls type I error and recovers relevant differences when applied to a specified point. Additionally, we demonstrate the ability to maintain power while controlling the family-wise error rate and false discovery rate when applying the test over a grid of points within the density space. Finally, we apply this method to a study of subjects with either multiple sclerosis (MS) or conditions that tend to mimic MS on MRI, and find significant differences between the two groups in their voxel intensity profiles within the thalamus.
The Power of Smiling: The Adult Brain Networks Underlying Learned Infant Emotionality
Authors: Stark Eloise A , Cabral Joana , Riem Madelon M E , Van IJzendoorn Marinus H , Stein Alan , Kringelbach Morten L 
Published: December 2019

AbstractThe perception of infant emotionality, one aspect of temperament, starts to form in infancy, yet the underlying mechanisms of how infant emotionality affects adult neural dynamics remain unclear. We used a social reward task with probabilistic visual and auditory feedback (infant laughter or crying) to train 47 nulliparous women to perceive the emotional style of six different infants. Using functional neuroimaging, we subsequently measured brain activity while participants were tested on the learned emotionality of the six infants. We characterized the elicited patterns of dynamic functional brain connectivity using Leading Eigenvector Dynamics Analysis and found significant activity in a brain network linking the orbitofrontal cortex with the amygdala and hippocampus, where the probability of occurrence significantly correlated with the valence of the learned infant emotional disposition. In other words, seeing infants with neutral face expressions after having interacted and learned their various degrees of positive and negative emotional dispositions proportionally increased the activity in a brain network previously shown to be involved in pleasure, emotion, and memory. These findings provide novel neuroimaging insights into how the perception of happy versus sad infant emotionality shapes adult brain networks.
Population Pharmacokinetics and Pharmacodynamics of Ciprofloxacin Prophylaxis in Pediatric Acute Lymphoblastic Leukemia Patients
Authors: Sassen S D T , Mathôt R A A , Pieters R , de Haas V , Kaspers G J L , van den Bos C , Tissing W J E , te Loo D M W W , Bierings M B , van Westreenen M , van der Sluis I M , Zwaan C M 
Published: December 2019

Abstract

Background
Ciprofloxacin is used as antimicrobial prophylaxis in pediatric acute lymphoblastic leukemia (ALL) to decrease infections with gram-negative bacteria. However, there are no clear guidelines concerning prophylactic dose.

Aims
To determine the pharmacokinetics and pharmacodynamics (PKPD) of ciprofloxacin prophylaxis in a pediatric ALL population. The effect of patient characteristics and antileukemic treatment on ciprofloxacin exposure, the area under the concentration time curve over minimal inhibitory concentration (AUC24/MIC) ratios, and emergence of resistance were studied.

Methods
A total of 615 samples from 129 children (0–18 years) with ALL were collected in a multicenter prospective study. A population pharmacokinetic model was developed. Microbiological cultures were collected prior to and during prophylaxis. An AUC24/MIC of ≥125 was defined as target ratio.

Results
A 1-compartment model with zero-order absorption and allometric scaling best described the data. No significant (P &lt; .01) covariates remained after backward elimination and no effect of asparaginase or azoles were found. Ciprofloxacin AUC24 was 16.9 mg*h/L in the prednisone prophase versus 29.3 mg*h/L with concomitant chemotherapy. Overall, 100%, 81%, and 18% of patients at, respectively, MIC of 0.063, 0.125, and 0.25 mg/L achieved AUC24/MIC ≥ 125. In 13% of the patients, resistant bacteria were found during prophylactic treatment.

Conclusion
Ciprofloxacin exposure shows an almost 2-fold change throughout the treatment of pediatric ALL. Depending on the appropriateness of 125 as target ratio, therapeutic drug monitoring or dose adjustments might be indicated for less susceptible bacteria starting from ≥ 0.125 mg/L to prevent the emergence of resistance and reach required targets for efficacy.