bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis of new antenna arrays with arbitrary geometries based on the superformula
Authors: Amaireh Anas A. , Dib Nihad I. , Al-Zoubi Asem S.
Published: September 2022

The synthesis of antenna arrays with low sidelobe levels is needed to enhance the communication systems’ efficiency. In this paper, new arbitrary geometries that improve the ability of the antenna arrays to minimize the sidelobe level, are proposed. We employ the well-known superformula equation in the antenna arrays field by implementing the equation in the general array factor equation. Three metaheuristic optimization algorithms are used to synthesize the antenna arrays and their geometries; antlion optimization (ALO) algorithm, grasshopper optimization algorithm (GOA), and a new hybrid algorithm based on ALO and GOA. All the proposed algorithms are high-performance computational methods, which proved their efficiency for solving different real-world optimization problems. 15 design examples are presented and compared to prove validity with the most general standard geometry: elliptical antenna array (EAA). It is observed that the proposed geometries outperform EAA geometries by 4.5 dB and 10.9 dB in the worst and best scenarios, respectively, which proves the advantage and superiority of our approach.
An efficient reconfigurable peak cancellation model for peak to average power ratio reduction in orthogonal frequency division multiplexing communication system
Authors: Rathod Shivaji , Kanathur Ramaswamy Nataraj , Srikantaswamy Mallikarjunaswamy , Kanathur Ramaswamy Rekha
Published: September 2022

The peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) communication system will be reduced using reconfigurable peak cancellation (RPC). RPC will also aid in improves the error vector magnitude (EVM) and reduces adjacent channel leakage ratio (ACLR) in OFDM communication system. The proposed RPC design methodology and practical implementation using field programmable gate array (FPGA) are discussed. The proposed RPC has been demonstrated using VIRTEX-7 XC7Z100 dual-core FPGA device with less hardware difficulty and minimum utilization of FPGA resources. The proposed RPC improves the efficiency of OFDM communication process by reducing complementary cumulative distribution function (CCDF) with respect to instantaneous power in dB. A comparison analysis was done between the existing selective mapping (SLM) method with proposed RPS method with respect FPGA resource utilization. The proposed RPC is implemented using VIRTEX-7 XC7Z100 dual-core FPGA device. Its effectively utilizing sub-carriers, fast Fourier transform (FFT) filter, bandwidth, and sampling frequency. Due to parallel switching operation, it reduces the PAPR, ACLR and improves EVM in OFDM signal with less hardware complexity.
Visual and light detection and ranging-based simultaneous localization and mapping for self-driving cars
Authors: Abdelhafid El Farnane , Abdelkader Youssefi My , Ahmed Mouhsen , Rachid Dakir , Abdelilah El Ihyaoui
Published: September 2022

In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.
Double sliding window variance detection-based time-of-arrival estimation in ultra-wideband ranging systems
Authors: Nouali Ibrahim Yassine , Slimane Zohra , Abdelmalek Abdelhafid
Published: September 2022

Ultra-wideband (UWB) ranging via time-of-arrival (TOA) estimation method has gained a lot of research interests because it can take full advantage of UWB capabilities. Energy detection (ED) based TOA estimation technique is widely used in the area due to its low cost, low complexity and ease of implementation. However, many factors affect the ranging performance of the ED-based methods, especially, non-line-of-sight (NLOS) condition and the integration interval. In this context, a new TOA estimation method is developed in this paper. Firstly, the received signal is denoised using a five-level wavelet decomposition, next, a double sliding window algorithm is applied to detect the change in the variance information of the received signal, the first path (FP) TOA is then calculated according to the first variance sharp increase. The simulation results using the CM1 and CM2 IEEE 802.15.4a channel models, prove that our proposed approach works effectively compared with the conventional ED-based methods.
Permutation based load balancing technique for long term evolution advanced heterogeneous networks
Authors: Jaber Alam Mohammed , Gafur Abdul , Rashid Syed Zahidur , Sadeque Md. Golam , Kundu Diponkor , Syed Rosni
Published: September 2022

Traffic congestion has been one of the major performance limiting factors of heterogeneous networks (HetNets). There have been several load balancing schemes put up to solve this by balancing load among base stations (BSs), but they appear to be unfeasible due to the complexity required and other unsatisfactory performance aspects. Cell range extension (CRE) has been a promising technique to overcome this challenge. In this paper, a permutation based CRE technique is proposed to find the best possible formation of bias for BSs to achieve load balance among BSs. In comparison to the baseline scheme, results depict that the suggested method attains superior performance in terms of network load balancing and average throughput. The complexity of the suggested algorithm is considerably reduced in comparison to the proposed permutation based CRE method it is further modified from.
Towards a new hybrid approach for building document-oriented data warehouses
Authors: Moukhi Nawfal El , Azami Ikram El , Hajbi Soufiane
Published: September 2022

Schemaless databases offer a large storage capacity while guaranteeing high performance in data processing. Unlike relational databases, which are rigid and have shown their limitations in managing large amounts of data. However, the absence of a well-defined schema and structure in not only SQL (NoSQL) databases makes the use of data for decision analysis purposes even more complex and difficult. In this paper, we propose an original approach to build a document-oriented data warehouse from unstructured data. The new approach follows a hybrid paradigm that combines data analysis and user requirements analysis. The first data-driven step exploits the fast and distributed processing of the spark engine to generate a general schema for each collection in the database. The second requirement-driven step consists of analyzing the semantics of the decisional requirements expressed in natural language and mapping them to the schemas of the collections. At the end of the process, a decisional schema is generated in JavaScript object notation (JSON) format and the data loading with the necessary transformations is performed.
Communication, culture, competency, and stakeholder that contribute to requirement elicitation effectiveness
Authors: Chaipunyathat Ajchareeya , Bhumpenpein Nalinpat
Published: September 2022

In the context of software development, requirement engineering is one of the crucial phases that leads to software project success or failure. According to several disruptive changes in the software engineering landscape as well as the world’s challenge of virus pandemic, the provision of practical and innovative software applications is required. Therefore, issues resolution in requirement elicitation is potentially one of the key success factors resulting in enhanced quality of system requirement. The authors have striven to create new ways of requirement elicitation according to factor effects of communication, culture, competency, and stakeholder, by incorporating tools, processes, methods, and techniques to solve the problems comprehensively, and then proposed an adaptive and applicable conceptual framework. To illustrate these effects, the authors performed a literature review from the past 8 years, and then data analysis from interviews of 27 practitioners, observations and focus groups of software development in real-life projects.
Best practice framework for information technology security governance in Indonesian government
Authors: Yuliana Rika , Arifin Hasibuan Zainal
Published: September 2022

Information technology security is crucial for a digital government system to have so that the continuity of business processes can run smoothly. However, the current best practice of information security governance in the Indonesian national government is still inadequate according to various related studies still siloed and scattered and leading to vulnerabilities in the various digital services provided. Therefore, this study aims to develop a best practice framework for managing information security that is aligned with the needs of Indonesia's digital government. This research started by looking for the main framework of information security governance. Then the main components that resulted from that were benchmarked with other Information Security Governance (ISG) best practices from different countries. Finally, it ended up complementing them with information security parameters, other related components, and recommendations, particularly in the Indonesian context, so that the main components and their respective constituent sub-components can be obtained according to the needs of the Indonesian e-government. The cause-and-effect analysis concept analyses the data linkages between the six central components and their respective sub-components. This study concludes that each of main components and sub-components supports each other so that all these things must be carried out in a balanced and continuous manner.
Digital watermarking by utilizing the properties of self-organization map based on least significant bit and most significant bit
Authors: Kadhim Jabbar Khalid , Tuieb Munthir Bahir , Thajeel Salam A.
Published: September 2022

Information security is one of the most important branches concerned with maintaining the confidentiality and reliability of data and the medium for which it is transmitted. Digital watermarking is one of the common techniques in this field and it is developing greatly and rapidly due to the great importance it represents in the field of reliability and security. Most modern watermarking systems, however, use the self-organization map (SOM), which is safer than other algorithms because an unauthorized user cannot see the result of the SOM's training. Our method presents a semi-fragile watermark under spatial domain using least significant bit (LSB) and by relying on most significant bit (MSB) when the taken values equal to (2 or 4 bits) depending on the characteristics of SOM through developing the so-called best matching unit (BMU) which working to determine the best location for concealment. As a result, it shows us the ability of the proposed method to maintain the quality of the host with the ability to retrieve data, whether it is a binary image or a secret message.
Speech emotion recognition using 2D-convolutional neural network
Authors: Reggiswarashari Fauzivy , Sihwi Sari Widya
Published: September 2022

This research proposes a speech emotion recognition model to predict human emotions using the convolutional neural network (CNN) by learning segmented audio of specific emotions. Speech emotion recognition utilizes the extracted features of audio waves to learn speech emotion characteristics; one of them is mel frequency cepstral coefficient (MFCC). Dataset takes a vital role to obtain valuable results in model learning. Hence this research provides the leverage of dataset combination implementation. The model learns a combined dataset with audio segmentation and zero padding using 2D-CNN. Audio segmentation and zero padding equalize the extracted audio features to learn the characteristics. The model results in 83.69% accuracy to predict seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise from the combined dataset with the segmentation of the audio files.
An optimal design of current conveyors using a hybrid-based metaheuristic algorithm
Authors: Abi Soufiane , Benhala Bachir
Published: September 2022

This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.
Selective local binary pattern with convolutional neural network for facial expression recognition
Authors: Zulkarnain Syavira Tiara , Suciati Nanik
Published: September 2022

Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.
Deep learning for cancer tumor classification using transfer learning and feature concatenation
Authors: Hassan Abdallah Mohamed , El-Mashade Mohamed Bakry , Aboshosha Ashraf
Published: September 2022

Deep convolutional neural networks (CNNs) represent one of the
 state-of-the-art methods for image classification in a variety of fields. Because the number of training dataset images in biomedical image classification is limited, transfer learning with CNNs is frequently applied. Breast cancer is one of most common types of cancer that causes death in women. Early detection and treatment of breast cancer are vital for improving survival rates. In this paper, we propose a deep neural network framework based on the transfer learning concept for detecting and classifying breast cancer histopathology images. In the proposed framework, we extract features from images using three pre-trained CNN architectures: VGG-16, ResNet50, and Inception-v3, and concatenate their extracted features, and then feed them into a fully connected (FC) layer to classify benign and malignant tumor cells in the histopathology images of the breast cancer. In comparison to the other CNN architectures that use a single CNN and many conventional classification methods, the proposed framework outperformed all other deep learning architectures and achieved an average accuracy of 98.76%.
Development of a web-based single-phase load monitoring and auditing system
Authors: Adebisi Oluwaseun Ibrahim , Adejumobi Isaiah Adediji , Matthew Simeon , Abdulsalam Azeez Aderibigbe
Published: September 2022

In a developing nation like Nigeria, the conventional load monitoring and billing system has proved to be tedious, time-consuming, expensive, and prone to human error over the years. Therefore, this creates the need for an efficient system that can assist the Utility to monitor the energy consumption trend of the customers remotely. This work developed a web-based single-phase load monitoring and auditing system using NodeMCU (ESP8266) microcontroller, PZEM-004T sensor, and liquid crystal display (LCD) module for the hardware unit and Blynk internet of things (IoT) platform for the software unit. The system design was implemented around the ESP8266 microcontroller with relevant design models, and standard power and energy equations programmed into the microcontroller in the Arduino integrated development environment. The developed system was load tested to examine its performance and determine its reading error. The hardware and software units of the system operated satisfactorily when tested. The reading accuracy for current and voltage measured by the device were ±0.2% and ±0.4%, respectively, giving a reading error of ±0.8% for power measurement. The developed system is suitable for residential, commercial, and similar applications where the energy usage trend of some small loads is required for management purposes.
An integrated home energy management system by the load aggregator in a microgrid using the internet of things infrastructure
Authors: Hosseini Seyed Ali , Hojjat Mehrdad , Azarfar Azita
Published: September 2022

Smart technologies enable the significant participation of consumers in demand-side management programs. In this paper, the management of electrical energy consumption for a set of residential houses in a microgrid by a load aggregator for a 24-h planning horizon is studied. In this study, consumption management programs are implemented on controllable equipment by sending binary codes by the load aggregator via the internet of things (IoT) infrastructure to residential sockets. To increase the level of customer convenience and provide more flexibility for consumers to participate in demand response programs, a parameter called the value of lost load (VOLL) has been introduced. According to the results, in addition to no need to use the energy management system for each residential house, only by moving shiftable loads to off-peak hours, 18.34% of energy consumption costs are saved daily. Also, from the load aggregator’s viewpoint for every 10% change in status from normal to the scheduled priority, there is a reduction of about 3.4% in the consumer’s peak-load cost. If solar arrays and storage resources are used, more than 18% of the total consumption cost can be saved.
Bridging the gap between the semantic web and big data: answering SPARQL queries over NoSQL databases
Authors: El Massari Hakim , Mhammedi Sajida , Gherabi Noreddine
Published: September 2022

Nowadays, the database field has gotten much more diverse, and as a result, a variety of non-relational (NoSQL) databases have been created, including JSON-document databases and key-value stores, as well as extensible markup language (XML) and graph databases. Due to the emergence of a new generation of data services, some of the problems associated with big data have been resolved. In addition, in the haste to address the challenges of big data, NoSQL abandoned several core databases features that make them extremely efficient and functional, for instance the global view, which enables users to access data regardless of how it is logically structured or physically stored in its sources. In this article, we propose a method that allows us to query non-relational databases based on the ontology-based access data (OBDA) framework by delegating SPARQL protocol and resource description framework (RDF) query language (SPARQL) queries from ontology to the NoSQL database. We applied the method on a popular database called Couchbase and we discussed the result obtained.
Matrix-matrix multiplication on graphics processing unit platform using tiling technique
Authors: Balagafshe Rahman Ghasempour , Akoushideh Alireza , Shahbahrami Asadollah
Published: September 2022

Today’s hardware platforms have parallel processing capabilities and many parallel programming models have been developed. It is necessary to research an efficient implementation of compute-intensive applications using available platforms. Dense matrix-matrix multiplication is an important kernel that is used in many applications, while it is computationally intensive, especially for large matrix sizes. To improve the performance of this kernel, we implement it on the graphics processing unit (GPU) platform using the tiling technique with different tile sizes. Our experimental results show the tiling approach improves speed by 56.89% (2.32× faster) against straightforward (STF). And tile size of 32 has the highest speed compared to other tile sizes of 8 and 16.
Threat modeling in application security planning citizen service complaints
Authors: Tedyyana Agus , Ratnawati Fajar , Syam Elgamar , Putra Fajri Profesio
Published: September 2022

The mobile-based service complaint application is one way to implement good governance today. Public facilitated to make complaints without going through a complicated process. Security aspects must be considered to protect user privacy. The security design must be considered so that no one is harmed by the application's users damaged in the application's use. This study used threat modeling during the planning stage of developing a citizen service complaint application to obtain information about vulnerabilities. The researcher uses the threat modeling process that the open web application security project (OWASP) organization has formulated as a framework. The researchers took steps to describe application information, determine and rank threats, countermeasures, and mitigation. In the final stage, the spoofing, tampering, repudiation, information disclosure, denial of service and elevation of privilege (STRIDE) threat modeling methodology is used to analyze and assess mitigation actions against threats in the application. The researcher gets a defense strategy to reduce the danger based on the threat analysis results. Threat modeling in the early phase software development life cycle process is constructive in ensuring that software is developed with adequate security based on threat mitigation from the beginning.
Determination of support vector regression parameters using African buffalo optimization algorithm
Authors: Maijama’a Inusa Sani , Yusof Yuhanis , Mohsin Mohamad Farhan
Published: September 2022

The use of support vector regression (SVR) for regression tasks has been on increase over the past few years. Unfortunately, the practical application of SVR for regression task is limited due to its dependence on proper setting of its hyper-parameters and associated kernel parameter. Therefore, it become imperative to device a reliable and fast mechanism of determining the value of these parameters that could guarantee lowest generalization error. This paper presents SVR parameter optimization approaches using African buffalo optimisation (ABO) algorithm, i.e. SVR-ABO. The SVR parameters are optimized by using African buffalo optimisation algorithm. Results obtained from several experiments performed has shown that the proposed ABO algorithm has the capability of determining SVR hyper-parameters which most of time has to be done through estimation.
A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset
Authors: Khalaf Hamoud Alaa , Baqr Mohammed Kamel Mohammed , Sahl Gaafar Alaa , Salah Alasady Ali , Majeed Humadi Aqeel , Akeel Awadh Wid , Mohammed Dahr Jasim
Published: September 2022

The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling Technique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' performance. The SMOTE filter is examined before and after applying feature selection approaches to measure the model's accuracy with supervised and unsupervised algorithms. Three supervised/unsupervised algorithms are examined based on feature selection approaches to predict the students' performance. The findings show that supervised algorithms (LMT, Simple Logistic, and Random Forest) got high accuracy after applying SMOTE without feature selection. The prediction accuracies of unsupervised algorithms (Canopy, EM, and Farthest First) are enhanced after applying feature selection approaches and SMOTE filter.
Firefly optimized robust, imperceptible, secure watermarking scheme
Authors: Agrawal Sushama , Bhalchandra Anjali
Published: September 2022

A multi-objective optimized hybrid image watermarking technique is being proposed considering robustness, imperceptibility and security aspects using two different scaling factors. In this technique, original image is subjected to third level lifting wavelet transform (LWT) followed by singular value decomposition (SVD). Watermark is split into two parts to embed each of them into a different subband. In the suggested scheme, firefly algorithm is employed to get optimum solutions for two scaling factors to balance trade-off amid invisibility and robustness. Security in digitized data is an important aspect of image processing. It is improved with a key, an input to Arnold transform for scrambling watermark, to watermark embedding and extraction procedures. All the performance parameters like peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation coefficient (NCC) and bit error rate (BER) are used in formulating maximization objective function. Evaluation of the proposed algorithm indicates that it is characterized by fairly good robustness, invisibility and security.
Synthesized carbon material with semi-organic and aqueous electrolyte for supercapacitor application
Authors: P. Moholkar Avinash , S. Bankar Deepak , B. Karandikar Parshuram
Published: September 2022

The supercapacitor is an evolving technology that has a potential to play a vital role in the today’s electrical energy storage systems. Supercapacitors are dominated by unique properties that can accompany other energy storage strategies due to its functional characteristics - such as quick charge/discharge ability, higher power density, very long cycle life. It results in to improved system efficiency of hybrid electric energy systems with reduced environmental pollution levels. In this paper, efforts are made to fabricate a low cost prototype of supercapacitor using synthesised carbon material obtained from the pyrolyzed scrap tyre by using both aqueous and Aloe vera gel as semi-aqueous/semi-organic electrolyte. The test results are obtained in the laboratory to ascertain the characteristics of developed supercapacitor in terms of capacitance, internal resistance and peak current. The obtained results are superior as compare to Vulcan XC-72R with trade-off between peak discharge current and other parameters. New carbon material obtained from scrap tyres is cost effective when used along with Aloe vera gel as an electrolyte. Use of scrap tyre carbon as electrode material and Aloe vera gel as naturally available material for electrolyte is an innovation of the presented work.   
The impacts from Sr4La(PO4)3O: Ce3+, Tb3+, Mn2+ phosphor resulting in luminous flux of WLED devices
Authors: Phuc Dang Huu , Van Hien Bui , Le Thai Nguyen
Published: September 2022

Using the solid-state technique that involves great temperature, we created multiple phosphors Sr4La(PO4)3O:Ce3+, Tb3+, Mn2+ (abbreviated as SLPO:Ce, Tb, Mn). We then examined their heat consistency, luminescence, as well as energy shift from Ce3+ to Tb3+ and Mn2+. We can acquire a considerable boost for the faint emission in green generated by Tb3+ as well as the emission in red generated by Mn2+ via adding the sensitizer Ce3+ ions. Through modifying the proportion between Ce3+ and Tb3+ along with the proportion between Ce3+ and Mn2+, it is possible to adjust the chroma of emission. We acquired white illumination which had color coordinates determined as (0.3326, 0.3298) for the testing phosphor Sr4La(PO4)3O: 0.12Ce3+, 0.3Mn2+. Such result displays the promising efficiency of phosphors Sr4La(PO4)3O:Ce3+, Tb3+, Mn2+ for the WLED devices.
Intelligent voltage controller based on firefly algorithm for DC-DC boost converter
Authors: Darmansyah Darmansyah , Robandi Imam
Published: September 2022

DC-DC boost converter is one of the important tools of photovoltaic (PV) power plant. DC-DC boost converter can be used to adjust the voltage output of photovoltaic before going to the inverter. In addition, DC-DC boost converter can be used as additional devices to make the PV plant operated in maximum power point condition. To get the optimal voltage adjusment, an appropriate controller is essential. Generally, PI controller is commonly used to control the switching procedure of the boost converter. However, with uncertainty of the source (uncertainty of the PV output), PI controller is out off dated. Hence designing PI controller based on the metaheuristic algorithm such as firefly algorithm is essential. This paper is proposed an intelligent voltage controller for DC-DC boost converter based on firefly algorithm (FA). From the simulation results it is noticeable that the PI based on FA could provide better control signal (indicated by the fastest settling time of the boost converter).
Enhancement of spectrum sensing technique with energy harvesting for cognitive radio network
Authors: Seun Okediran Oladapo , Kehinde Ojo Festus , Adedapo Ojo John , Fatai Oseni Olasunkanmi , Oladele Obanisola Oluwole
Published: September 2022

Spectrum Sensing (SS) is cognitive radio's (CR’s) fundamental and essential mechanism to locate idle spectrum. Multipath effects in wireless channels, on the other hand, reduce CR's sensing accuracy, resulting in a significant risk of missing data. Optimal Spectrum Sensing Technique with Energy Harvesting (OSSTEH) used to solve this problem suffers from poor performance due to only one Primary User (PU) branch used. Hence, in this paper, an enhanced SS with energy harvesting technique is proposed using Maximal Ratio Combiner (MRC). The multiple copies of the PU signal were received using 'N' number of secondary users (SU) antennas and divided into two equal fractions. The first fraction was used for spectrum sensing, while the latter was used for energy harvesting. The first set of SU antennas received a PU signal, which was combined using MRC, and the output of the combiner was used as an input to the energy detector to calculate the received signal's energy. To assess the existence or absence of the PU signal, the acquired energy was compared to the threshold limit. The enhanced technique was evaluated using the probability of detection and signal strength compared with conventional OSSTEH. The enhanced technique gave a high detection rate with an increase in charging rate than the conventional OSSTEH.
Classification of lower limb rehabilitation exercises with multiple and individual inertial measurement units
Authors: S. Tanna Rashmin , H. Vithalani Chandulal
Published: September 2022

Straight leg raise rehabilitation exercises (for both lying and seated position) for lower limb injuries play a critical role in terms of stress on joints after the injury. The primary objective of the paper is to find how accurately and efficiently a single and a two IMU sensor-based system could classify SSLR (Seated straight leg raise) and LSLR (Lying straight leg raise) exercises using machine learning. Inertial Measurement Units (IMUs) that include accelerometer and gyroscope were calibrated and tested, individual and combined, for classified seating as well as lying exercise and for different demanded personalities. Individual IMUs achieved about 96 % accuracy in binary classification. However, the combined (two) IMUs achieved about 96.8 % accuracy. The merits of the proposed IMU based sensor system are that it is easy to install, cost effective and very useful for telemedical operations in pandemic situations like COVID19. On the basis of these results, it could be concluded that the accuracy of a single IMU sensor system and a two IMU sensor-based system is approximately 96% and both were efficiently able to classify SSLR and LSLR exercises as well as identify the individual performing the exercise.
Towards a new method of estimating the student attention based on the eye gaze
Authors: Hachad Tarik , Sadiq Abdelalim , Ghanimi Fadoua , Hachad Lamiae , Laguidi Ahmed
Published: September 2022

This paper presents a new system for automating the monitoring and estimation of student attention during the course session. The followed approach is based on the analysis of the student's gaze to predict his state of attention. A simple hardware device consisting of a camera and a pc was used in this study. Existing machine learning algorithms were used for the student gaze estimation. The principles of homography were used to ensure the transformation from an image coordinates system to a real-world coordinates system. 5 students took part in this experiment and whose gaze was detected and analyzed during 10 minutes of the class session in order to analyze their states of attention and inattention.
A systematic literature review of automatic ontology construction
Authors: Zafirah Zulkipli Zayanah , Maskat Ruhaila , Hasimah Ibrahim Teo Noor
Published: September 2022

In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.
Computer modeling and simulation to predict COVID-19 propagation patterns via factual cellular automata
Authors: M. Al-Tabbakh Shahinaz , Karim Marwa A.
Published: September 2022

Computer modelling and simulation methods are very important and play a critical role in the mitigation and response to the ongoing COVID-19 pandemic. In this study, we propose a computational modeling technique based on Cellular Automata (CA) with realistic proposed rules. The rules are designed to simulate the propagation of COVID-19 disease through a bounded area. Our proposed CA rules are novel in many respects. For on, the classification of neighbors to nearest neighbors and range of neighbors based on cellular layers is explained. Moreover, the concepts of time generation and access time are deployed for the first time to model the propagation of the disease over time in this work. Further details of the proposed model including the topology of the defined area, the initial states of the cells and four-layer transfer mechanism are explained as well. This work may be considered a criterion of spreading for COVID-19 from point source in a defined population area. The results of the proposed algorithm represent the percentage of the population whose infectious status is described by different cellular state objects after a defined generation time. The results are compared under different circumstances and analyzed equanimity.
Mathematical and computer simulation of particle redistribution and inertial swarming in dispersed systems
Authors: Rashidovich Ismailov Bakhtiyar , Mukhamedjanovna Musabekova Leyla , Rysbayevna Umarova Zhanat , Bakhtiyarovich Ismailov Khairulla , Arystanbayev Kuttybek
Published: September 2022

This work is devoted to modeling the motion of a dispersed phase in a gas flow in two cases: i) the dynamic characteristics of the gas flow (velocity components, stream functions and vorticity, ii) are found by numerically solving the Navier-Stokes equations with the subsequent construction of the droplet trajectory; study of the coalescence of droplets in a given model gas flow with analysis of the swarming of dispersed particles. In the first approach, the size and location of vortex and stagnant zones in a channel with a streamlined plate and with a turning flow are determined by numerically solving the equations of gas dynamics in Helmholtz variables. To study the coalescence of drops that occurs when the trajectories of two or more drops come into contact, a second approach has been developed-a computer model of inertial swarming in dispersed systems using the Delphi10 programming environment based on stochastic lattice algorithms and computable lattice enlargement. This approach is needed to pre-define the media flow rate profile. Also in this article, were analyzed the grouping and redistribution of particles for the manifestation of the "swarm of drops" effect.
Comprehensive Cardiovascular Magnetic Resonance‐Derived Myocardial Strain Analysis Provides Independent Prognostic Value in Acute Myocarditis
Authors: Vos Jacqueline L. , Raafs Anne G. , van der Velde Nikki , Germans Tjeerd , Biesbroek Paul Stefan , Roes Kit , Hirsch Alexander , Heymans Stephane R. B. , Nijveldt Robin
Published: September 2022

Background

Late gadolinium enhancement and left ventricular (LV) ejection fraction on cardiovascular magnetic resonance (CMR) are prognostic markers, but their predictive value for incident heart failure or life‐threatening arrhythmias in acute myocarditis patients is limited. CMR‐derived feature tracking provides a more sensitive analysis of myocardial function and may improve risk stratification in myocarditis. In this study, the prognostic value of LV, right ventricular, and left atrial strain in acute myocarditis patients is evaluated.

Methods and Results

In this multicenter retrospective study, patients with CMR‐proven acute myocarditis were included. The primary end point was occurrence of major adverse cardiovascular events: all‐cause mortality, heart transplantation, heart failure hospitalizations, and life threatening arrhythmias. LV global longitudinal strain, global circumferential strain and global radial strain, right ventricular‐global longitudinal strain and left atrial strain were measured. Unadjusted and adjusted cox proportional hazard regression analysis were performed. In total, 162 CMR‐proven myocarditis patients were included (41 ± 17 years, 75% men). Mean LV ejection fraction was 51 ± 12%, and 144 (89%) patients had presence of late gadolinium enhancement. Major adverse cardiovascular events occurred in 29 (18%) patients during a follow‐up of 5.5 (2.2–8.3) years. All LV strain parameters were independent predictors of outcome beyond clinical features, LV ejection fraction and late gadolinium enhancement (LV‐global longitudinal strain: hazard ratio [HR] 1.07,
P
=0.02; LV‐global circumferential strain: HR 1.15,
P
=0.02; LV‐global radial strain: HR 0.98,
P
=0.03), but right ventricular or left atrial strain did not predict outcome.

Conclusions

CMR‐derived LV strain analysis provides independent prognostic value on top of clinical parameters, LV ejection fraction and late gadolinium enhancement in acute myocarditis patients, while left atrial and right ventricular strain seem to be of less importance.
Feasibility of Quantitative Flow Ratio Virtual Stenting for Guidance of Serial Coronary Lesions Intervention
Authors: Guan Shaofeng , Gan Qian , Han Wenzheng , Zhai Xinrong , Wang Ming , Chen Yang , Zhang Liang , Li Tianqi , Chang Xifeng , Liu Hongyuan , Hong Weilin , Li Zehang , Tu Shengxian , Qu Xinkai
Published: September 2022

Background

Coronary physiology measurement in serial coronary lesions with multiple stenoses is challenging. Therefore, we evaluated the feasibility of Murray fractal law‐based quantitative flow ratio (μQFR) virtual stenting for guidance of serial coronary lesions intervention.

Methods and Results

Patients who underwent elective coronary angiography and had 2 serial de novo coronary lesions of 30% to 90% diameter stenosis by visual estimation were prospectively enrolled. μQFR and fractional flow reserve (FFR) were assessed after coronary angiography. In vessels with an FFR ≤0.80, the lesion with the larger pressure gradient was considered to be the primary lesion and treated firstly, followed by FFR measurement. The second lesion was stented when FFR ≤0.80. All μQFR and predicted μQFR after stenting were calculated from diagnostic coronary angiography before interventions, with the analysts masked to the FFR data. A total of 54 patients with 61 target vessels were interrogated. Percutaneous coronary intervention was performed in 44 vessels with FFR ≤0.80. After stenting the primary lesions, 14 nonprimary lesions had FFR ≤0.80 and a second drug‐eluting stent was implanted. There was excellent correlation (
r
=0.97,
P
<0.001) and good agreement (mean difference: 0.00±0.03) between baseline μQFR and FFR in identifying flow‐limiting lesions. Per‐vessel diagnostic accuracy of μQFR on de novo lesions was 96.7% (95% CI, 88.7%–99.6%). μQFR and FFR are highly consistent (93.2%) in identifying the primary lesion requiring revascularization. After stenting the primary lesions, per‐vessel diagnostic accuracy of predicted μQFR for identifying the significance of the nonprimary lesion was 90.9%. Predicted residual μQFR with virtual stenting was higher than final FFR (mean difference: 0.05±0.06).

Conclusions

In vessels with serial coronary lesions, virtual stenting by μQFR can identify the primary flow‐limiting lesion for revascularization.
Safety in coils: predation rates of ambush hunting rattlesnakes are extremely low
Authors: Maag Dylan , Clark Rulon
Published: September 2022

Rattlesnakes are widespread mesopredators that are themselves killed and eaten by a host of other predators, including birds of prey and carnivorous mammals. Although anecdotal accounts of rattlesnake depredation are common, there are few quantitative data on encounter rates between rattlesnakes and their predators. Here we review a large database of encounters between rattlesnakes and their predators recorded from field videography of snakes in the sit-and-wait phase of their ambush hunting strategy. We found that, across 8300 hours of observation, adult rattlesnakes of six species and multiple populations exhibit low encounter rates with predators; furthermore, when predators were encountered, we never observed them to attack or kill coiled snakes. Thus, we propose that rattlesnakes are preyed upon while performing other, riskier behaviors associated with moving through the landscape. We also discuss why rattlesnakes are at low risk of predation while hunting on the surface.
Practical guidelines for quantitative wood anatomy on Ginkgo biloba L.
Authors: Huang Weiwei , Li Yueyi
Published: September 2022

Ginkgo biloba L., a long-lived relict species, has high ecological, cultural and economic value. Studying the quantitative wood anatomy of G. biloba is important to understand the relationships between their tree-growth and climate change, which could help in the protection of old G. biloba trees. Preparing high-quality wood thin sections is the premise of quantitative wood anatomy analysis. In addition, manual correction of image analysis is a time-consuming process. We therefore present a protocol on how to make G. biloba high-quality xylem thin sections, from sample preparation, sample embedding, blade selection to thickness selection. We also present a method to determine the necessary sample width and number of cores to establish reliable wood anatomical time series that both can capture climate signals and reduce the time used for image analysis. For microsection preparation, it is optimal to cut 20–30-μm-thick sections using Leica DB80 blades after applying the cornstarch solution on top of the sample surface. For image analysis, the reliable wood anatomical time series can be established when measuring six cores (trees) or above, and the environmental signals are captured when analyzing a 1.2-mm-wide radial strip along the increment core (deviation <2%) for the parameters cell density, mean lumen area, potential hydraulic conductivity and percentage of the conductive area. This paper provides practical guidance to successfully use quantitative wood anatomy methods on G. biloba. We hope our study will be helpful for future research on the quantitative wood anatomy of G. biloba and similar tree species.