bluuebunny/crossref_metadata_2025 huggingface dataset:
GRU-CNN Neural Network Method for Regional Traffic Congestion Prediction Serving Traffic Diversion Demand
Authors: Xing Xue , Li Xiaoyu , Zhai Yaqi
Published: September 2022

Predicting spatiotemporal congestion situations of a traffic network is a prerequisite for urban traffic control. This study proposes a spatiotemporal traffic congestion situation prediction method based on the recurrent gated unit-convolutional neural network (GRU-CNN). Considering the time and space attributes of traffic data, the third-order tensor of the traffic data is extracted from the time domain, and the GRU is used to predict the traffic flow parameters of the traffic network. Then, the third-order tensor of multisource spatiotemporal traffic data is compressed into traffic data images and combined with the spatial structure. The feature extraction technology of a CNN is used to extract and identify the traffic network congestion features. Actual urban traffic network data are selected for model verification. The multistep prediction of the traffic flow parameters effectively ensures prediction accuracy. The proposed model is trained by the actual classification dataset. The prediction results of the test set demonstrate the model’s reliability. Based on predicting the traffic parameters of the network, this model can give a highly accurate judgment of the traffic situation for the entire network. Compared with other models, the proposed model further improves the accuracy of road network traffic state discrimination and has better robustness.
An Empirical Study on Digital Feedback Behavior of Young People in COVID-19 with Health Belief Model and UTAUT Model
Authors: Lu Dan , Lin Huhuang
Published: September 2022

During COVID-19 prevention and control, many middle-aged and elderly people lack the skills and knowledge of healthy online transmission, resulting in inadequate prevention and control measures. So as young people who are familiar with the rules of the internet transmit health information, they also reverse educate their parents on novel coronavirus prevention and control, helping them to bridge the digital information gap between generations. In this paper, the health belief model (HBM) and the integrated technology acceptance and use model (UTAUT) were combined to establish a model to explore the factors influencing the digital feedback behavior of young groups in the context of COVID-19. The results show that the willingness of digital feedback has a certain influence on digital feedback behavior. Perceived susceptibility, perceived severity, perceived benefit, perceived obstacle, self-efficacy, performance expectation, community influence, and convenience also affect the intention of feedback.
Efficient Inverse Model for Cloud Computing and Information Based on the Symmetric Random Matrix
Authors: Cheng Ning , Cheng Yan
Published: September 2022

With the continuous development of cloud storage technology, cloud storage has become a new way of storage for both enterprises and individuals. The cloud environment can support users to share data, but it can lead to some malicious users accessing or modifying data by illegal means, so access control is an important way to protect user data. In this paper, we propose a traceable attribute encryption scheme based on signature authentication and based on the information efficient inverse model of an asymmetric random matrix, which can trace the user who leaks information. A user attribute revocation scheme based on improved attribute encryption is also proposed, which enables data owners to reduce computational overhead. In cloud storage access control, the workload of the data owner increases exponentially when facing a high number of user attribute revocation problems, which are jointly decided by the data owner and the authorization center. An error proportional allocation (EPA) calculation method is developed to achieve an optimal estimation of system parameters in the lattice cipher scheme; experimental results show that the scheme has the advantages of shorter parameters, efficient computation, and lower storage load, proving that the scheme is resistant to IND-SAS-CPA under the standard model-based assumption of determining LWE semantic security against IND-SAS-CPA attacks; and it is used in a cloud file sharing (CFS) service framework, which can make sensitive cloud data free from the risk of privacy leakage.
Preclinical Characterization and Phase I Study of an Anti–HER2-TLR7 Immune-Stimulator Antibody Conjugate in Patients with HER2+ Malignancies
Authors: Janku Filip , Han Sae-Won , Doi Toshihiko , Amatu Alessio , Ajani Jaffer A. , Kuboki Yasutoshi , Cortez Alex , Cellitti Susan E. , Mahling Ping C. , Subramanian Kulandayan , Schoenfeld Heidi A. , Choi Sarah M. , Iaconis Lori A. , Lee Lang Ho , Pelletier Marc R. , Dranoff Glenn , Askoxylakis Vasileios , Siena Salvatore
Published: September 2022

Immune-stimulator antibody conjugates (ISAC) combining tumor-targeting monoclonal antibodies with immunostimulatory agents allow targeted delivery of immune activators into tumors. NJH395 is a novel, first-in-class ISAC comprising a Toll-like receptor 7 (TLR7) agonist conjugated to an anti-HER2 antibody via a noncleavable linker payload. Preclinical characterization showed ISAC-mediated activation of myeloid cells in the presence of antigen-expressing cancer cells, with antigen targeting and TLR7 agonism contributing to antitumor activity. Safety, efficacy, immunogenicity, pharmacokinetics, and pharmacodynamics were investigated in a phase I, multicenter, open-label study in patients with HER2+ non-breast advanced malignancies (NCT03696771). Data from 18 patients enrolled in single ascending dose escalation demonstrated delivery of the TLR7-agonist payload in HER2+ tumor cells and induction of type I IFN responses, which correlated with immune modulation in the tumor microenvironment. Cytokine release syndrome was a common, but manageable, drug-related adverse event. Antidrug antibodies and neuroinflammation at high doses represented significant clinical challenges. Data provide proof-of-mechanism and critical insights for novel immunotherapies.
Outcomes of Mild Stroke and High-Risk Transient Ischemic Attack in Current Clinical Practice
Authors: Wesanonthawech Apiluk , Dharmasaroja Pornpatr A.
Published: September 2022

Introduction: Early assessment and management of patients with mild stroke and transient ischemic attack (TIA) by specialists were recommended. This study aimed to evaluate the outcomes of these patients and identify the predictive factors of clinical progression, unfavorable outcomes, and recurrent stroke. Methods: Patients with mild ischemic stroke (NIHSS ≤5) and high-risk TIA were studied. All patients were managed by stroke specialists within 24 h of stroke onset. The outcomes of the patients at 3 months and final follow-up were studied. Predictive factors of clinical progression, unfavorable outcomes, and recurrent stroke were analyzed. Results: 254 patients were studied. Thirty-eight patients (15%) had clinical progression during admission. Large artery atherosclerosis (OR 2.49, 95% CI: 1.06–5.81), cardioembolism (OR 3.34, 95% CI: 1.26–8.87), and brainstem stroke (OR 2.78, 95% CI: 1.28–6.01) were associated with clinical progression. At the final follow-up, median 22 months, 81 patients (32%) had unfavorable outcomes. Previous disability (OR 1.81, 95% CI: 3.31–100), moderate to severe white matter lesions (OR 2.90, 95% CI: 1.44–5.84), clinical progression (OR 12.5, 95% CI: 5.08–31.25), and recurrent stroke (OR 8.47, 95% CI: 3.21–22.72) were related to unfavorable outcomes. Eleven patients (4%) had recurrent stroke within 3 months and 31 patients (12%) at the final follow-up. Older age (OR 6.68, 95% CI: 2.35–19.02), diabetes mellitus (OR 2.59, 95% CI: 1.07–6.27), and smoking (OR 4.26, 95% CI: 1.52–11.95) were related to recurrent stroke. Conclusion: Implementation of the up-to-date standard care in clinical practice would bring good clinical outcomes to the patients with mild stroke and high-risk TIA.
Professional development teacher to improve skills of science process and creativity of learners
Authors: Amrulloh Muhammad Sholahuddin , Galushasti Andarula
Published: September 2022

This qualitative research emphasized learning strategies in the process of full engagement by students and discovering the implementation of learning, science process skills, and learners' creativity. The selection of contextual teaching and learning methods due to teachers having a role more in strategy affairs while learners were more focused on self-actualization of practice in their groups. Here, there was an interaction between teachers and learners to complement each other. The participant was student of class five Elementary School/Madrasah Ibtidaiyah Ma'arif 37 Sunan Kalijogo, Ambulu, Jember Regency, Indonesia. Methods of data collection were interviews, observations, and documentation. Data collection was related to natural science subjects. The study results showed the procedures for implementing contextual teaching and learning in natural science subjects. Applying contextual teaching and learning methods was very effective in natural science subjects. The findings of this study showed that improving the quality of learning and learning support facilities can realize the ability of competence of students and teacher professionalism.
Students’ perspective on online learning during the COVID-19 pandemic
Authors: Irawaty Irawaty , Momo Abdul Halim , Sulfa Sulfa , Salimin Salimin , Nerlin Nerlin , Mustar Sri Yuliani
Published: September 2022

The COVID-19 pandemic has impacted changing the model of learning activities. Teaching and learning activities that have usually been done face-to-face have now turned into online learning. This study identified student perspectives on online learning activities for Department of Civics Education students at the Faculty of Teacher Training and Education at Halu Oleo University, Indonesia during the COVID-19 pandemic. This study used a survey method by distributing questionnaires in the form of Google Forms to students, and a total of 227 students participated in this study. Our study focused on online learning media, learning effectiveness, lecturers' abilities, obstacles, and student recommendations regarding online learning during the COVID-19 pandemic. Data analysis in this study was carried out descriptively, presented in the form of frequency, percentage, and graph. The study results found that the most effective online learning media felt by students at the Department of Civics Education were Google Meet and Zoom Cloud Meetings. Besides, the majority of students considered online learning to be no more effective than face-to-face lectures. Lecturers need to provide two-way learning materials through discussion as well as question and answer. Furthermore, the main determining factors that cause online learning to be ineffective are network constraints and internet quotas. Therefore, students expected the lecturers to understand the network constraints experienced by students during online learning and recommend implementing learning activities for blended learning (combination of online and offline) while still complying with the health protocols.
Teaching experience on online learning in higher education: Generational analysis
Authors: Kesumaningsari Ni Putu Adelia , Pudjibudojo Jatie K. , Louk Maya Hilda Lestari
Published: September 2022

A transition in learning model implementation occurred in higher education during the COVID-19 pandemic. Online learning answers the needs of long-distance learning. However, not all higher education institutions are thoroughly prepared for online learning. Therefore, this study aimed to understand the online learning experience at higher education institutions from the perspectives of multigenerational teaching staff. This study utilized mixed-method research through a quantitative and qualitative study. A total of 93 participants; male (n=40) and female (n=57) teaching staff filled out a research questionnaire consisting of closed and open questions about online learning. The data were analyzed using descriptive statistics and qualitative thematic analysis. The results indicated positive and negative contributions of the online learning process that affect the effectiveness of online learning according to multigenerational teaching staff perspectives. These findings contribute to the global discussion about the online learning process in higher education and recommend the usage of online learning for teaching staff across generations, complemented with an effective instructional design that would help provide a rigorous learning environment.
International Journal of Electrical and Computer Engineering: a bibliometric analysis
Authors: Garcés-Gómez Yeison Alberto , Henao-Céspedes Vladimir
Published: September 2022

This study is focused on analyzing seven years of bibliometric data of the International Journal of Electrical and Computer Engineering (IJECE) from 2014 to 2020. The analysis of 2,928 papers exhibits multi-folded growth of 34.25%, rising from 109 articles in 2014 to 638 articles by 2020. In addition, the analysis of the structure of publications as well as the mapping of bibliographic data based on co-citation, bibliographic coupling, and co-occurrence showed the intellectual structure and connection between universities, countries, and contributing authors. As the journal’s first retrospective, this study not only educates and enriches IJECE’s global readership and aspiring contributors, but may also be useful to its editorial board, as it provides several inputs for navigating future research.
Recursive convex approximations for optimal power flow solution in direct current networks
Authors: Ocampo-Toro Jauder Alexander , Montoya Oscar Danilo , Grisales-Noreña Luis Fernando
Published: September 2022

The optimal power flow problem in direct current (DC) networks considering dispersal generation is addressed in this paper from the recursive programming point of view. The nonlinear programming model is transformed into two quadratic programming approximations that are convex since the power balance constraint is approximated between affine equivalents. These models are recursively (iteratively) solved from the initial point vt equal to 1.0 pu with t equal to 0, until that the error between both consecutive voltage iterations reaches the desired convergence criteria. The main advantage of the proposed quadratic programming models is that the global optimum finding is ensured due to the convexity of the solution space around vt. Numerical results in the DC version of the IEEE 69-bus system demonstrate the effectiveness and robustness of both proposals when compared with classical metaheuristic approaches such as particle swarm and antlion optimizers, among others. All the numerical validations are carried out in the MATLAB programming environment version 2021b with the software for disciplined convex programming known as CVX tool in conjuction with the Gurobi solver version 9.0; while the metaheuristic optimizers are directly implemented in the MATLAB scripts.
Analytical transformations software for stationary modes of induction motors and electric drives
Authors: Qawaqzeh Mohamed Zaidan , Miroshnyk Oleksandr , Osichev Aleksandr , Tkachenko Andrii , Danylchenko Dmytro
Published: September 2022

A program was developed in the package of symbolic transformations Maple. It provides automatic analytical transformation and derivation of formulas and plotting of the main characteristics of induction motors (IM) in a convenient form for an electrical engineer and student: torque=f(slip) T=f(s), angular speed=f(Torque) ω=f(T), angular speed=f(Current) ω=f(I), current=f(slip) I=f(s); cos(φ) and phase angle (phi) φ for stator currents and rotor currents, and magnetizing circuit, machine efficiency η=f(s) and a number of other characteristics. The calculation is based on the equivalent circuit of IM motors in its different variants: with one cage in the rotor, with two or more cages in the rotor, taking into account the skin effect in the rotor rods and without it. The user can build up the equivalent circuit to the desired configuration. The algorithm of further transformations is based on analytical obtaining of amplitude/frequency and phase/frequency characteristics in the nodes of the equivalent circuits with further calculation by power and slip. Online animation of the graphs with alternate variations of all resistances R and inductances L values of the model is provided. The article contains screenshots of important parts of the programs and illustrates the complete set of graphs.
Comparison analysis of different classification methods of power quality disturbances
Authors: Shafiqa Zakaria Nur Adrinna , Said Dalila Mat , Rosmin Norzanah , Ahmad Nasarudin , Shah Jamil Mohamad Shazwan , Mirsaeidi Sohrab
Published: September 2022

Good power quality delivery has always been in high demand in power system utilities where different types of power quality disturbances are the main obstacles. As these disturbances have distinct characteristics and even unique mitigation techniques, their detection and classification should be correct and effective. In this study, eight different types of power quality disturbances were synthetically generated, by using a mathematical approach. Then, continuous wavelet transform (CWT) and discrete wavelet transform with multi-resolution analysis (DWT-MRA) were applied, which eight features were then extracted from the synthesized signals. Three classifiers namely, decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) were trained to classify these disturbances. The accuracy of the classifiers was evaluated and analyzed. The best classifier was then integrated with the full model, which the performance of the proposed model was observed with 50 random signals, with and without noise. This study found that wavelet-transform was effective to localize the disturbances at the instant of their occurrence. On the other hand, the SVM classifier is superior to other classifiers with an overall accuracy of 94%. Still, the need for these classifiers to be further optimized is crucial in ensuring a more effective detection and classification system.
Modified sunflower optimization for network reconfiguration and distributed generation placement
Authors: Nguyen Thuan Thanh , Nguyen Ngoc Anh , Duong Thanh Long , Ngo Thanh Quyen , Bach Thanhquy
Published: September 2022

This paper proposed modified sunflower optimization (MSFO) for the combination of network reconfiguration and distributed generation placement problem (NR-DGP) to minimize power loss of the electric distribution system (EDS). Sunflower optimization (SFO) is inspired form the ideal of sunflower plant motion to get the sunlight and its reproduction. To enhance the performance of SFO, it is modified to MSFO wherein, the pollination and mortality techniques have been modified by using Levy distribution and mutation of the best solutions. The results are evaluated on two test systems. The efficiency of MSFO is compared with that of the original SFO and other algorithms in literature. The comparisons show that MSFO outperforms to SFO and other methods in obtained optimal solution. Furthermore, MSFO demonstrates the better statistical results than SFO. So, MSFO can be a powerful approach for the NR-DGP problem.
An efficient scanning algorithm for photovoltaic systems under partial shading
Authors: Bahri Mohamed , Talea Mohamed , Bahri Hicham , Aboulfatah Mohamed
Published: September 2022

This paper proposes a new technique of maximum power point tracking (MPPT) for a photovoltaic (PV) system connected to three phase grids under partial shading condition (PSC), based on a new combined perturb and observe (P&O) with scanning algorithm. This new algorithm main advantages are the high-speed tracking compared to existing algorithms, high accuracy and simplicity which makes it ideal for hardware implementation. Simulation was carried on MATLAB/Simulink. Results showed the effectiveness in speed and accuracy of our algorithm over the existing ones either during standard condition (STC) or PSC. Furthermore, conventional direct power control (DPC) was applied to synchronize successfully the injected power with the grid, which makes our algorithm global and works efficiently under severe conditions.
Grouping based radio frequency identification anti-collision protocols for dense internet of things application
Authors: Umelo Nnamdi H. , Noordin Nor K. , A. Rasid Mohd Fadlee , Geok Tan K. , Hashim Fazirulhisyam
Published: September 2022

Radio frequency identification (RFID) is an important internet of things (IoT) enabling technology. In RFIDs collision occur among tags because tags share communication channel. This is called tag collision problem. The problem becomes catastrophic when dense population of tags are deployed like in IoT. Hence, the need to enhance existing dynamic frame slotted ALOHA (DFSA) based electronic product code (EPC) C1G2 media access control (MAC) protocol. Firstly, this paper validates through simulation the DFSA theory that efficiency of the RFID system is maximum when the number tags is approximately equal to the frame size. Furthermore, literature review shows tag grouping is becoming popular to improving the efficiency of the RFID system. This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. The algorithms were implemented in MATLAB while extensive Monte Carlo simulation was performed to evaluate their strengths and weaknesses. Results show that with higher tag density, fuzzy C-means based algorithm (FCMBG) outperformed traditional DFSA by over 40% in terms of throughput rate. The results also demonstrate FCMBG bettered other grouping-based algorithms (GB-DFSA and GBSA) whose tag estimation method are based on collision slots in terms slot efficiency by over 10% and also in terms of identification time.
Three-phase four-wire shunt hybrid active power filter model with model predictive control in imbalance distribution networks
Authors: Andang Asep , Hartati Rukmi Sari , Gede Manuaba I Bagus , Satya Kumara I Nyoman
Published: September 2022

This paper presents a harmonic reduction and load imbalance model in a three-phase four-wire distribution network. This model uses a hybrid active power filter, a passive inductor and capacitor filter, and an active power filter in the form of a three-phase, four-leg connected grid inverter. The switching of the voltage source converter on this filter uses finite control set model predictive control (FCS-MPC). Control of this hybrid active power filter uses model predictive control (MPC) with a cost function, comparing the reference current and prediction current with mathematical modelling of the circuit. The reference current is taken from the load current by extracting dq, and the predicted current is obtained from the iteration of the voltage source converter (VSC) switching pattern. Each combination is compared with the reference current in the cost function to get the smallest error used as a power switching signal. Modelling was validated by using MATLAB Simulink. The simulation results prove a decrease in harmonics at a balanced load from 22.16% to 4.2% and at an unbalanced load, reducing the average harmonics to 4.74%. The simulation also decreases the load current imbalance in the distribution network. Reducing the current in the neutral wire from 62.01%-0.42% and 11.29-0.3 A.
Energy management for hybrid electric vehicles using rule based strategy and PI control tuned by particle swarming optimization algorithm
Authors: Al-Flehawee Maher , Al-Mayyahi Auday
Published: September 2022

Recently, hybrid electric vehicles are increasingly being used to replace conventional vehicles. In this paper, a control methodology is designed that can reduce fuel consumption and improve the vehicle’s dynamic response. As the control unit based on this methodology consists of two levels, the first depends on the application of a rule-based strategy for energy management between the main components of the vehicle, and this strategy is based on a set of rules that are activated according to parameters such as vehicle speed and the battery state of charge (SOC) that control the activation/deactivation of the internal combustion engine (ICE), motor, and generator. This level also makes ICE operate at operation points with high efficiency, which is represented by the optimal operating line (OOL). The second level is called the low control level, and it consists of two proportional-integral (PI) controllers used to control the speed of each ICE and the motor to obtain the appropriate torque for both of them to drive the vehicle properly. The particle swarming optimization (PSO) algorithm is utilized to tune the parameters of the PI controllers. The obtained results have effectively minimized fuel consumption and improved the performance of the vehicle.
3D printing part orientation optimization: discrete approximation of support volume
Authors: Guacheta Alba Juan C. , Gonzalez Garzon Sebastian , Nunez Diego A. , Mauledoux Mauricio , Aviles Oscar F.
Published: September 2022

In three-dimensional (3D) printing, due to the geometry of most parts, it is necessary to use extra material to support the manufacturing process. This material must be discarded after printing, so its reduction is essential to minimize manufacturing time and cost. An important parameter that must be defined before starting the printing process is the part orientation, which has repercussions on the quality, deposition path, and post-processing among others. Usually, the user sets up this parameter arbitrarily, so this paper takes advantage of it on optimization techniques and proposes an approximation of the volume be covered by the support material, which depends directly on the angle of the part to be printed and its geometry. Among mono-objectives optimization strategies, this work focuses on five of them. Their performance is compared by two metrics: support volume and execution time. Then, the best result is compared with commercial software.
A survey on bio-signal analysis for human-robot interaction
Authors: Radha Huda Mustafa , Hassan Alia Karim Abdul
Published: September 2022

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems.
Modeling and analysis of field-oriented control based permanent magnet synchronous motor drive system using fuzzy logic controller with speed response improvement
Authors: Mooloor Sahridayan Parvathy Thampi , Gopal Raghavendra
Published: September 2022

The permanent magnet synchronous motor (PMSM) acts as an electrical motor mainly used in many diverse applications. The controlling of the PMSM drive is necessary due to frequent usage in various systems. The conventional proportional-integral-derivative (PID) controller’s drawbacks are overcome with fuzzy logic controller (FLC) and adopted in the PMSM drive system. In this manuscript, an efficient field-oriented control (FOC) based PMSM drive system using a fuzzy logic controller (FLC) is modeled to improve the speed and torque response of the PMSM. The PMSM drive system is modeled using abc to αβ and αβ to abc transformation, 2-level space vector pulse width modulation (SVPWM), AC to DC rectifier with an inverter, followed by PMSM drive, proportional integral (PI) controller along with FLC. The FLC’s improved fuzzy rule set is adopted to provide faster speed response, less % overshoot time, and minimal steady-state error of the PMSM drive system. The simulation results of speed response, torque response, speed error, and phase currents are analyzed. The FLC-based PMSM drive is compared with the conventional PID-based PMSM drive system with better improvements in performance metrics.
Internet of things implementation and analysis of fuzzy Tsukamoto in prototype irrigation of rice
Authors: Silalahi Lukman Medriavin , Jatikusumo Dimas , Budiyanto Setiyo , Silaban Freddy Artadima , Simanjuntak Imelda Uli Vistalina , Rochendi Agus Dendi
Published: September 2022

This research raises the topic of modern technology in the field of rice fields. The problem in this research is determining the fuzzy inference system algorithm for electronic engineering. The prototype was built by Raspberry Pi and python-based to the internet of things. The objective of this research is to design a new model for the rice field monitoring/control system and display every condition based on the internet of things. So that the hypothesis of this research can answer the phenomena that occur in rice fields, including drought problems, maintained plant conditions. The test results showed that irrigation control automatically runs optimally by scheduling, automatic irrigation control of water pH degree value detection analyzed by fuzzy Tsukamoto method at Z=3.5 defuzzification value for low and high irrigation control, and Z value=1.83 for normal irrigation control. Furthermore, the scheduling of spraying liquid fertilizer obtained the results of duration for 60 min in accordance with the needs of fertilizer dose. Lastly, for monitoring data on the website successfully accessed anywhere from the use of hosting servers and domains. Finally, it can be concluded that fuzzy Tsukamoto's algorithm is appropriate to be applied to the modern rice field system.
Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer
Authors: Jasim Sarah Saadoon , Abdul Hassan Alia Karim
Published: September 2022

In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.
Precise identification of objects in a hyperspectral image by characterizing the distribution of pure signatures
Authors: M Panchal Soumyashree , Shivaputra Shivaputra
Published: September 2022

Hyperspectral image (HSI) has been widely adopted in many real-world applications due to its potential to provide detailed information from spectral and spatial data in each pixel. However, precise classification of an object from HSI is challenging due to complex and highly correlated features that exhibit a nonlinear relationship between the acquired spectral unique to the HSI object. In literature, many research works have been conducted to address this problem. However, the problem of processing high-dimensional data and achieving the best resolution factor for any set of regions remains to be evolved with a suitable strategy. Therefore, the proposed study introduces simplified modeling of the hyperspectral image in which precise detection of regions is carried out based on the characterization of pure signatures based on the estimation of the maximum pixel mixing ratio. Moreover, the proposed system emphasizes the pixel unmixing problem, where input data is processed concerning wavelength computation, feature extraction, and hypercube construction. Further, a non-iterative matrix-based operation with a linear square method is performed to classify the region from the input hyperspectral image. The simulation outcome exhibits efficient and precise object classification is achieved by the proposed system in terms classified HSI object and processing time.
Hand geometry recognition: an approach for closed and separated fingers
Authors: Kehinde Adeniyi Jide , Omolewa Oladele Tinuke , Oluwatobi Noah Akande , Tunde Taiwo Adeniyi
Published: September 2022

Hand geometry has been a biometric trait that has attracted attention from several researchers. This stems from the fact that it is less intrusive and could be captured without contact with the acquisition device. Its application ranges from forensic examination to basic authentication use. However, restrictions in hand placement have proven to be one of its challenges. Users are either instructed to keep their fingers separate or closed during capture. Hence, this paper presents an approach to hand geometry using finger measurements that considers both closed and separate fingers. The system starts by cropping out the finger section of the hand and then resizing the cropped fingers. 20 distances were extracted from each finger in both separate and closed finger images. A comparison was made between Manhattan distance and Euclidean distance for features extraction. The support vector machine (SVM) was used for classification. The result showed a better result for Euclidean distance with a false acceptance ratio (FAR) of 0.6 and a false rejection ratio (FRR) of 1.2.
Recognition of compound characters in Kannada language
Authors: Tumkur Narasimhaiah Sridevi , Rangarajan Lalitha
Published: September 2022

Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes.
Implementation of the advanced encryption standard algorithm on an FPGA for image processing through the universal asynchronous receiver-transmitter protocol
Authors: Prasanna Talapala Lakshmi , Siddaiah Nalluri , Krishna Boppana Murali , Valluri Maheswara Rao
Published: September 2022

Communication among end users can be based either on wired or wireless technology. Cryptography plays a vital role in ensuring data exchange is secure among end users. Data can be encrypted and decrypted using symmetric or asymmetric key cryptographic techniques to provide confidentiality. In wireless technology, images are exchanged through low-cost wireless peripheral devices, such as radio frequency identification device (RFID), nRF, and ZigBee, that can interface with field programmable gate array (FPGA) among the end users. One of the issues is that data exchange through wireless devices does not offer confidentiality, and subsequently, data can be lost. In this paper, we propose a design and implementation of AES-128 cipher algorithm on an FPGA board for image processing through the universal asynchronous receiver transmitter (UART) protocol. In this process, the advanced encryption standard (AES) algorithm is used to encrypt and decrypt the image, while the transmitter and receiver designs are implemented on two Xilinx BASYS-3 circuits connected with a ZigBee RF module. The encrypted image uses less memory, such as LUTs (141), and also consumes less chip power (0.0291 w), I/O (0.003), block RAM (0.001 w), data, and logic to provide much higher efficiency than wired communication technology. We also observe that images can be exchanged through the UART protocol with different baud rates in run time.
A simplified and novel technique to retrieve color images from hand-drawn sketch by human
Authors: Narasimha Murthy Pavithra , Yeliyur Hanumanthaiah Sharath Kumar
Published: September 2022

With the increasing adoption of human-computer interaction, there is a growing trend of extracting the image through hand-drawn sketches by humans to find out correlated objects from the storage unit. A review of the existing system shows the dominant use of sophisticated and complex mechanisms where the focus is more on accuracy and less on system efficiency. Hence, this proposed system introduces a simplified extraction of the related image using an attribution clustering process and a cost-effective training scheme. The proposed method uses K-means clustering and bag-of-attributes to extract essential information from the sketch. The proposed system also introduces a unique indexing scheme that makes the retrieval process faster and results in retrieving the highest-ranked images. Implemented in MATLAB, the study outcome shows the proposed system offers better accuracy and processing time than the existing feature extraction technique.
Social distance and face mask detector system exploiting transfer learning
Authors: Sadanand Vijaya Shetty , Anand Keerthi , Suresh Pooja , Kadyada Arun Kumar Punnya , Mahabaleshwar Priyanka
Published: September 2022

As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.
Emotion recognition based on the energy distribution of plosive syllables
Authors: Agrima Abdellah , Mounir Ilham , Farchi Abdelmajid , Elmazouzi Laila , Mounir Badia
Published: September 2022

We usually encounter two problems during speech emotion recognition (SER): expression and perception problems, which vary considerably between speakers, languages, and sentence pronunciation. In fact, finding an optimal system that characterizes the emotions overcoming all these differences is a promising prospect. In this perspective, we considered two emotional databases: Moroccan Arabic dialect emotional database (MADED), and Ryerson audio-visual database on emotional speech and song (RAVDESS) which present notable differences in terms of type (natural/acted), and language (Arabic/English). We proposed a detection process based on 27 acoustic features extracted from consonant-vowel (CV) syllabic units: \ba, \du, \ki, \ta common to both databases. We tested two classification strategies: multiclass (all emotions combined: joy, sadness, neutral, anger) and binary (neutral vs. others, positive emotions (joy) vs. negative emotions (sadness, anger), sadness vs. anger). These strategies were tested three times: i) on MADED, ii) on RAVDESS, iii) on MADED and RAVDESS. The proposed method gave better recognition accuracy in the case of binary classification. The rates reach an average of 78% for the multi-class classification, 100% for neutral vs. other cases, 100% for the negative emotions (i.e. anger vs. sadness), and 96% for the positive vs. negative emotions.
Design and characterization of frequency reconfigurable honey bee antenna for cognitive radio application
Authors: Murshed Abu Hena , Hossain Md. Azad , Nishiyama Eisuke , Toyoda Ichihiko
Published: September 2022

In this article, a frequency reconfigurable honey-bee compact microstrip monopole antenna is proposed which is fed by a microstrip line (50 Ω) having the capability of providing dual-band as well as triple-band operation in eight distinct modes. By embedding three PIN diodes overs the honey bee arms, the effective current distribution is controlled hence resonant frequency is also changed in eight distinct modes in real-time. This is the reason the proposed antenna is portrayed as a frequency reconfigurable antenna in this paper which is suitable for cognitive radio application. This proposed antenna can be used for various wireless application such as Bluetooth, Wi-Fi, worldwide interoperability for microwave access (WiMAX), wireless local area network (WLAN), C-band, and X-band applications. The proposed antenna possesses a planner geometry of 39×34×0.87 mm3 which is printed on a substrate as flexible FR-4 (lossy) (εr=4.4 and tanδ=0.019). The proposed antenna exhibits voltage standing wave ratio (VSWR)<2 for all 19 resonant frequencies of interest and perceptible radiation pattern over entire frequency bands with a positive gain. CST microwave studio is used to find out all simulated results of antenna parameters.
Embedded iron object detection using asynchronous full wave envelope detector technique in ground penetrating radar system
Authors: Razali Maryanti , Joret Ariffuddin , Sulong Muhammad Suhaimi , Abdullah Mohammad Faiz Liew , Baharudin Elfarizanis , Che Ku Melor Che Ku Nor Azie Hailma , Zulkefli Nur Izzati , Shairi Noor Azwan
Published: September 2022

The use of a ground penetrating radar (GPR) system that operates at low frequencies allows the detection of embedded objects underground from the earth’s surface deeper than high frequency. However, the output signal generated from the system using pulse modulation (PM) technique and high-frequency carrier, has many high ripple signals consequently resulting in a blurry image. Nevertheless, this ripple signal can be minimized by reprocessing the signal using an envelope detector method. In this study, an envelope detection technique called ArJED© asynchronous full-wave (AFW) was used in the GPR system and was tested at a frequency range from 0.06 to 0.08 GHz. A dipole antenna has been used as an embedded object detection sensor of the GPR system. The detection system of embedded objects involves four depths starting with 2 cm depth, 5 cm, 7 cm, and 20 cm. A comparison of embedded object images before and after the application of the envelope detection technique was done and proved that the proposed envelope detection technique has produced a clearer radargram image of the GPR system.
Underwater localization and node mobility estimation
Authors: Mudhafar Saif Khalid , Abdelkareem Ammar Ebdelmelik
Published: September 2022

In this paper, localizing a moving node in the context of underwater wireless sensor networks (UWSNs) is considered. Most existing algorithms have had designed to work with a static node in the networks. However, in practical case, the node is dynamic due to relative motion between the transmitter and receiver. The main idea is to record the time of arrival message (ToA) stamp and estimating the drift in the sampling frequency accordingly. It should be emphasized that, the channel conditions such as multipath and delay spread, and ambient noise is considered to make the system pragmatic. A joint prediction of the node mobility and speed are estimated based on the sampling frequency offset estimation. This sampling frequency offset drift is detected based on correlating an anticipated window in the orthogonal frequency division multiplexing (OFDM) of the received packet. The range and the distance of the mobile node is predicted from estimating the speed at the received packet and reused in the position estimation algorithm. The underwater acoustic channel is considered in this paper with 8 paths and maximum delay spread of 48 ms to simulate a pragmatic case. The performance is evaluated by adopting different nodes speeds in the simulation in two scenarios of expansion and compression. The results show that the proposed algorithm has a stable profile in the presence of severe channel conditions. Also, the result shows that the maximum speed that can be adopted in this algorithm is 9 km/h and the expansion case profile is more stable than the compression scenario. In addition, a comparison with a dynamic triangular algorithm (DTN) is presented in order to evaluate the proposed system.
Monte Carlo simulation convergences’ percentage and position in future reliability evaluation
Authors: Rusyda Roslan Nur Nabihah , Mohd Fauzi NoorFatin Farhanie , Muhammad Ridzuan Mohd Ikhwan
Published: September 2022

Reliability assessment is a needed assessment in today's world. It is required not only for system design but also to ensure the power delivered reaches the consumer. It is usual for fault to occur, but it is best if the fault can be predicted and the way to overcome it can be prepared in advance. Monte Carlo simulation is a standard method of assessing reliability since it is a time-based evaluation that nearly represents the actual situation. However, sequential Monte Carlo (SMC) typically took long-time simulation. A convergence element can be implemented into the simulation to ensure that the time taken to compute the simulation can be reduced. The SMC can be done with and without convergence. SMC with convergence has high accuracy compared to the SMC without convergence, as it takes a long time and has a high possibility of not getting accurate output. In this research, the SMC is subjected to five different convergence items to determine which converge simulation is the fastest while providing better performance for reliability evaluation. There are two types of convergence positions, namely input convergence and output convergence. Overall, output convergence shows the best result compared to input convergence.