bluuebunny/crossref_metadata_2025 huggingface dataset:
Stepper, Swipe, Tilt, Force
Authors: Ren Yuan , Arif Ahmed Sabbir
Published: November 2021

Picking numbers is arguably the most frequently performed input task on smartwatches. This paper presents three new methods for picking numbers on smartwatches by performing directional swipes, twisting the wrist, and varying contact force on the screen. Unlike the default number picker, the proposed methods enable users to actively switch between slow-and-steady and fast-and-continuous increments and decrements during the input process. We evaluated these methods in two user studies. The first compared the new methods with the default input stepper method in both stationary and mobile settings. The second compared them for individual numeric values and values embedded in text. In both studies, swipe yielded a significantly faster input rate. Participants also found the method faster, more accurate, and the least mentally and physically demanding compared to the other methods. Accuracy rates were comparable between the methods.
Multi-channel Tactile Feedback Based on User Finger Speed
Authors: Rekik Yosra , Lank Edward , Guettaf Adnane , Grisoni Laurent
Published: November 2021

Alongside vision and sound, hardware systems can be readily designed to support various forms of tactile feedback; however, while a significant body of work has explored enriching visual and auditory communication with interactive systems, tactile information has not received the same level of attention. In this work, we explore increasing the expressivity of tactile feedback by allowing the user to dynamically select between several channels of tactile feedback using variations in finger speed. In a controlled experiment, we show that a user can learn the dynamics of eyes-free tactile channel selection among different channels, and can reliable discriminate between different tactile patterns during multi-channel selection with an accuracy up to 90% when using two finger speed levels. We discuss the implications of this work for richer, more interactive tactile interfaces.
Worlds to Die Harder For Open Oracle Questions for the 21st Century
Authors: Fortnow Lance
Published: November 2021

Most of the interesting open problems about relationships between complexity classes have either been resolved or have relativizable worlds in both directions. We discuss some remaining open questions, updating questions from a similar 1995 survey of Hemaspaandra, Ramachandra and Zimand and adding a few new problems.
Five Ways Not To Fool Yourself
Authors: Harris Tim
Published: November 2021

Performance experiments are often used to show that a new system is better than an old system, and to quantify how much faster it is, or how much more efficient it is in the use of some resource. Frequently, these experiments come toward the end of a project and - at times - seem to be conducted more with the aim of selling the system rather than providing understanding of the reasons for the differences in performance or the scenarios in which similar improvements might be expected. Mistrust in published performance numbers follows from the suspicion that we measure what we have already optimized.
Electrochemical Determination of Chloramphenicol on Glassy Carbon Electrode Modified Activated Carbon Derived from Rice Husks
Authors: Thanh Tran Si , Thanh Tu Nguyen Thi , Quy Phan Tu , Toan Tran Thanh Tam , Thanh Nguyen Mau , Nguyen Vo Thang , Tuan Nguyen Hoang , Nhiem Dao Ngoc , Lieu Pham Khac , Khieu Dinh Quang
Published: November 2021

In the present article, the application of activated carbon derived from rice husks (RHAC) with high surface area as an electrode modifier for electrochemical determination of chloramphenicol (CP) is demonstrated. The RHAC was synthesized via a two-step alkaline pretreatment followed by the application of ultrasound radiation. The obtained carbon materials were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), nitrogen adsorption/desorption isotherms, Fourier transformation Infrared (FT-IR) spectroscopy and Raman spectroscopy. It was found that the ultrasonic activation process plays a critical role in improving the textural properties of AC by removing fine particles to clean capillaries and transforming a graphitic structure to a turbostratic one. The RHAC synthesized in suitable conditions displayed a specific surface area up to 1710 m2g−1. The glassy carbon electrode modified with RHAC exhibited an excellent electroactivity towards CP as compared to bare GCE. The voltammetry characteristics of the electrochemical sensor were studied by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). This electrode had a good performance with a linear response range of 0.95–5.76μM and a low detection limit of 0.66μM (S/N = 3). The present promising sensor displayed high selectivity, great stability and expectable recovery for CP detection in real samples.
Effect of D-Panthenol on Corneal Epithelial Healing after Surface Laser Ablation
Authors: Hamdi Islam Mahmoud
Published: November 2021

Purpose. To study the effect of D-panthenol (provitamin B5) on corneal epithelial healing, in cases of surface laser ablation. Patients and Methods: 45 eyes, of 45 patients undergoing laser surface ablation, received D-panthenol 2% in one eye and artificial tear drops of similar composition not containing D-panthenol in the other eye, postoperatively, for 2 months. Patients were examined daily for 3 days after the procedure. They were then examined weekly for 1 month. An additional examination was made after 2 months. Visual acuity (Log MAR) was assessed at every visit. Rate of healing (% of covered area) and subjective sensation of discomfort (scale 0–5) were assessed in the 1st 3 visits. Residual manifest cylinder (D) (as a parameter of corneal irregularity) and corneal clarity (epithelial and stromal haze) were assessed from week 1 to month 2. Results. During the first 3 days, both groups showed statistically nonsignificant (

P
>
0.05

) results. From week 1 to month 2, eyes receiving D-panthenol showed better vision and less residual cylinder (

P
<
0.05

) at week 1. For all other parameters, and at different examinations, both groups showed a statistically nonsignificant (

P
>
0.05

) difference. Still, eyes receiving D-panthenol showed better values at the majority of the parameters tested. Conclusion. D-Panthenol effect on corneal epithelial regeneration is of minimal clinical relevance. A different dosage and a larger sample of patients might reveal a statistical relevance. This trial is registered with https://doi.org/10.1186/ISRCTN81441126.
Magnetic Property and Therapeutic Effect of a New Co(II) Complex on Liver Cancer by Regulating the Expression of miRNA31
Authors: Guo Zhenshan , Zhen Jun , Yao Zhoujia , Wang Yemeng , Jin Chenlu
Published: November 2021

Employing the flexible hexacarboxylate ligand of 1,3,5-triazine-2,4,6-triamine hexaacetic acid (H6TTHA) to assemble with Co(NO3)2·6H2O, we have acquired a novel coordination compound, i.e., [Co2(H2TTHA)(H2O)]n·6n(H2O) (1). The analysis of single X-ray diffraction indicated that the H2TTHA2- ligand μ5-bridges connected the Co(II) ions into a two-dimensional layered architecture. Moreover, the magnetic property of 1 was also investigated between 2 and 300 K under 1000 Oe applied magnetic field. The novel compound’s inhibitory activity against the viability of cancer cell was determined through CCK-8 assay, and the expression of miRNA31 in liver cancer cells was detected via the real-time RT-PCR.
A Practical Design Method for Reducing Postconstruction Settlement of Highway Subgrade Induced by Soil Creep
Authors: Zhou Fei , Xia Tangdai , Yu Bingqi , Xia Fan , Yu Fan
Published: November 2021

The postconstruction settlement of the bridge approach is usually uneven, which could create a bump in the roadway. Indeed, this is a typical situation at the end of the bridge approach and requires a solution. One of the main causes of postconstruction settlement is the creep of soil. This paper is aimed at generalizing a new design method for controlling highway postconstruction settlement by replacing subgrade with expanded polystyrene (EPS). In the new method, the creep coefficient can be calculated based on the Yin-Graham EVP model. Thus, the relationship between the overloading ratio (OLR) and overconsolidation ratio (OCR) is obtained. The new method involves five steps: (a) determine the creep coefficient based on the relationship between the creep coefficient and over consolidation ratio, (b) divide the ground into a suitable number of sublayers, (c) select groups of different overloading ratios and then calculate the average values of the additional stress and overconsolidation ratio for each sublayer under different OLRs, (d) calculate the postconstruction settlement under different OLRs, and (e) determine the replacement capacity for different sections. This method can be used for quantitative design according to different requirements of postconstruction settlement of foundation. Taking Huzhou Avenue as an example, the case study illustrates the calculation process of the new method in detail.
Cosmetic “Gold Thread Therapy”: A Nuisance That Decreases the Diagnostic Quality of a Dental Panoramic Image
Authors: Ching Mah Eng , Joan Lim Zhi Yin , Nambiar Phrabhakaran
Published: November 2021

During routine imaging of the craniofacial region, it is recognised that some “cosmetic” procedures with metallic insertions can be revealed radiographically. These objects however make it difficult to obtain a good interpretation of anatomical structures for management of diseases. A 58-year-old female patient visited a private dental facility in Kuala Lumpur for prosthodontic replacement of missing teeth. The dental panoramic image revealed generalized bone loss and numerous unusual multiple thread-like radioopacities. These gold threads made radiographic evaluation difficult and complicated the process of treatment planning for dental implant placement advocated for this patient.
A Blockchain-Based CP-ABE Scheme with Partially Hidden Access Structures
Authors: Ba Yang , Hu Xuexian , Chen Yue , Hao Zenghang , Li Xuewei , Yan Xincheng
Published: November 2021

Data sharing has become a key technology to break down data silos in the big data era. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in secure data-sharing schemes to realize flexible and fine-grained access control. However, in traditional CP-ABE schemes, the access structure is directly shared along with the ciphertext, potentially leading to users’ private information leakage. Outsourcing data to a centralized third party can easily result in privacy leakage and single-point bottlenecks, and the lack of transparency in data storage and sharing casts doubts whether users’ data are safe. To address these issues, we propose a blockchain-based CP-ABE scheme with partially hidden access structures (BCP-ABE-PHAS) to achieve fine-grained access control while ensuring user privacy. First, we propose an efficient CP-ABE scheme with partially hidden access structures, where the ciphertext size is constant. To assist data decryption, we design a garbled Bloom filter to help users quickly locate the position of wildcards in the access structure. Then, to improve storage efficiency and system scalability, we propose a data storage scheme that combines blockchain technology and the interplanetary file system, ensuring data integrity. Finally, we employ smart contracts for a transparent data storage and sharing process without third-party participation. Security analysis and performance evaluation show that the proposed BCP-ABE-PHAS scheme can preserve policy privacy with efficient storage and low computational overhead.
Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization
Authors: Bo Li , Xuegang Luo , Junrui Lv
Published: November 2021

A new nonconvex smooth rank approximation model is proposed to deal with HSI mixed noise in this paper. The low-rank matrix with Laplace function regularization is used to approximate the nuclear norm, and its performance is superior to the nuclear norm regularization. A new phase congruency lp norm model is proposed to constrain the spatial structure information of hyperspectral images, to solve the phenomenon of “artificial artifact” in the process of hyperspectral image denoising. This model not only makes use of the low-rank characteristic of the hyperspectral image accurately, but also combines the structural information of all bands and the local information of the neighborhood, and then based on the Alternating Direction Method of Multipliers (ADMM), an optimization method for solving the model is proposed. The results of simulation and real data experiments show that the proposed method is more effective than the competcing state-of-the-art denoising methods.
A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era
Authors: Meng Xiangmin , Ren Guoyan , Huang Wenjun
Published: November 2021

This paper adopts a deep learning approach to analyze and study the mechanism of quantitative enhancement of college students’ employment and entrepreneurial abilities in the context of the digital era. The deep learning connotation is predetermined as five abilities, which are metacognitive ability, active communication and cooperation ability, deep processing ability, creative practice ability, and learning empathy experience, and, based on this, the deep learning questionnaire is designed, and it is reclassified by exploratory factor analysis to reduce the dimensionality, and the specific indicators and scientific connotation dimensions of the deep learning questionnaire are determined; and, through the deep learning of each dimension, the problems of deep learning of college students are examined and in-depth analysis is conducted, and the inner relationship and correlation among the dimensions of deep learning of college students are derived through correlation analysis. The success of innovation and entrepreneurship depends on the innovation and entrepreneurial ability of college students, and the formation of the ability influenced various factors. Therefore, not only is studying the influencing factors of college students’ innovation and entrepreneurship ability in line with the requirements of the times and social development, but also it can solve real problems. This thesis adopts a combination of two methods, qualitative research and quantitative research, to study the influencing factors of college students’ innovation and entrepreneurship ability and tries to ensure the scientificity, accuracy, and comprehensiveness of the conclusion. In this paper, we analyzed the requirements of the employment prediction system for graduating secondary school students, carried out the software framework and database design of the employment analysis and prediction system for secondary school students, and designed the system modularly based on the analysis results. By applying the proposed deep feedforward neural network prediction model to the prediction system, a software system applicable to the employment prediction and guidance of secondary school students is implemented.
Novel approach for hybrid MAC scheme for balanced energy and transmission in sensor devices
Authors: Gowda Anitha Krishna , Jayachandra Ananda Babu , Lingaraju Raviprakash Madenur , Rajkumar Vinay Doddametikurke
Published: November 2021

Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
A fuzzy-based prediction approach for blood delivery using machine learning and genetic algorithm
Authors: El Midaoui Marouane , Qbadou Mohammed , Mansouri Khalifa
Published: November 2021

Multiple diseases require a blood transfusion on daily basis. The process of a blood transfusion is successful when the type and amount of blood is available and when the blood is transported at the right time from the blood bank to the operating room. Blood distribution has a large portion of the cost in hospital logistics. The blood bank can serve various hospitals; however, amount of blood is limited due to donor shortage. The transportation must handle several requirements such as timely delivery, vibration avoidance, temperature maintenance, to keep the blood usable. In this paper, we discuss in first section the issues with blood delivery and constraint. The second section present routing and scheduling system based on artificial intelligence to deliver blood from the blood-banks to hospitals based on single blood bank and multiple blood banks with respect of the vehicle capacity used to deliver the blood and creating the shortest path. The third section consist on solution for predicting the blood needs for each hospital based on transfusion history using machine learning and fuzzy logic. The last section we compare the results of well-known solution with our solution in several cases such as shortage and sudden changes.
Impact of hybrid FACTS devices on the stability of the Kenyan power system
Authors: Mbae Mutegi , Nwulu Nnamdi
Published: November 2021

Flexible alternating current transmission system (FACTS) devices are deployed for improving power system’s stability either singly or as a combination. This research investigates hybrid FACTS devices and studies their impact on voltage, small-signal and transient stability simultaneously under various system disturbances. The simulations were done using five FACTS devices-static var compensator (SVC), static synchronous compensator (STATCOM), static synchronous series compensators (SSSC), thyristor controlled series compensator (TCSC) and unified power flow controller (UPFC) in MATLAB’s power system analysis toolbox (PSAT). These five devices were grouped into ten pairs and tested on Kenya’s transmission network under specific contingencies: the loss of a major generating machine and/or transmission line. The UPFC-STATCOM pair performed the best in all the three aspects under study. The settling times were 3 seconds and 3.05 seconds respectively for voltage and rotor angle improvement on the loss of a major generator at normal operation. The same pair gave settling times of 2.11 seconds and 3.12 seconds for voltage and rotor angle stability improvement respectively on the loss of a major transmission line at 140% system loading. From the study, two novel techniques were developed: A performance-based ranking system and classification for FACTS devices.
Utilization of idle time slot in spectrum sensing under noise uncertainty
Authors: Tumar Iyad Khalil , Arar Adnan Mohammad , El Saleh Ayman Abd
Published: November 2021

Spectrum sensing in cognitive radio (CR) is a critical process as it directly influences the accuracy of detection. Noise uncertainty affects the reliability of detecting vacant holes in the spectrum, thus limiting the access of that spectrum by secondary users (SUs). In such uncertain environment; SUs sense the received power of a primary user (PU) independently with different measures of signal-to-noise ratio (SNR). Long sensing time serves in mitigating the effect of noise uncertainty, but on the cost of throughput performance of CR system. In this paper, the scheme of an asynchronous and crossed sensing-reporting is presented. The scheme reduces energy consumption during sensing process without affecting the detection accuracy. Exploiting the included idle time (𝑇𝑖) in sensing time slot; each SU collects power samples with higher SNR directly performs the reporting process to a fusion center (FC) consecutively. The FC terminates the sensing and reporting processes at a specific sensing time that corresponds to the lowest SNR (𝑆𝑁𝑅𝑤𝑎𝑙𝑙). Furthermore, this integrated scheme aims at optimizing the total frame duration (𝑇𝑓). Mathematical expressions of the scheme are obtained. Analytical results show the efficiency of the scheme in terms of energy saving and throughput increment under noise uncerainty.
A hybrid algorithm for voltage stability enhancement of distribution systems
Authors: Mohsin Al-Wazni Hazim Sadeq , Abdulla Al-Kubragyi Shatha Suhbat
Published: November 2021

This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
Performance of symmetric and asymmetric links in wireless networks
Authors: Khamayseh Yaesr , Al-qudah Rabiah
Published: November 2021

Wireless networks are designed to provide the enabling infrastructure for emerging technological advancements. The main characteristics of wireless networks are: Mobility, power constraints, high packet loss, and lower bandwidth. Nodes’ mobility is a crucial consideration for wireless networks, as nodes are moving all the time, and this may result in loss of connectivity in the network. The goal of this work is to explore the effect of replacing the generally held assumption of symmetric radii for wireless networks with asymmetric radii. This replacement may have a direct impact on the connectivity, throughput, and collision avoidance mechanism of mobile networks. The proposed replacement may also impact other mobile protocol’s functionality. In this work, we are mainly concerned with building and maintaining fully connected wireless network with the asymmetric assumption. For this extent, we propose to study the effect of the asymmetric links assumption on the network performance using extensive simulation experiments. Extensive simulation experiments were performed to measure the impact of these parameters. Finally, a resource allocation scheme for wireless networks is proposed for the dual rate scenario. The performance of the proposed framework is evaluated using simulation.
Priority selection of agro-meteorological parameters for integrated plant diseases management through analytical hierarchy process
Authors: Patil Rutuja Rajendra , Kumar Sumit
Published: November 2021

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1).
Mutual query data sharing protocol for public key encryption through chosen-ciphertext attack in cloud environment
Authors: Lakum Tarasvi , Rao Barige Thirumala
Published: November 2021

In this paper, we are proposing a mutual query data sharing protocol (MQDS) to overcome the encryption or decryption time limitations of exiting protocols like Boneh, rivest shamir adleman (RSA), Multi-bit transposed ring learning parity with noise (TRLPN), ring learning parity with noise (Ring-LPN) cryptosystem, key-Ordered decisional learning parity with noise (kO-DLPN), and KD_CS protocol’s. Titled scheme is to provide the security for the authenticated user data among the distributed physical users and devices. The proposed data sharing protocol is designed to resist the chosen-ciphertext attack (CCA) under the hardness solution for the query shared-strong diffie-hellman (SDH) problem. The evaluation of proposed work with the existing data sharing protocols in computational and communication overhead through their response time is evaluated.
Solution for intra/inter-cluster event-reporting problem in cluster-based protocols for wireless sensor networks
Authors: Al-Zubi Raed Taleb , Kreishan Abdulraheem Ahmed , Alawad Mohammad Qasem , Darabkh Khalid Ahmad
Published: November 2021

In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
Misalignment fading effects on the ACC performance of relay-assisted MIMO/FSO systems over atmospheric turbulence channels
Authors: Duong Huu Ai , Nguyen Van Loi , Luong Khanh Ty
Published: November 2021

The continuous development of internet of things (IoT) technology enables many devices to be interconnected through the external environment. Meanwhile, 5G technology provides an enhanced quality of services with high data transmission rates, requiring IoT implementation in the 5G architecture. Free-space optical communication (FSO) is considered a promising technique that can provide high-speed communication links, so FSO is an optimal choice for wireless networks to fulfill the full potential of 5G technology, providing speeds of 100 Gb/s or more. By implementing 5G features in IoT, IoT coverage and performance will be enhanced by using FSO models. Therefore, the paper proposed and investigated the multiple-input and multiple-output/free-space optical communication (MIMO/FSO) model using subcarrier quadrature amplitude modulation (SC-QAM) and relay stations over atmospheric turbulence channels by log-normal and gamma-gamma distribution under different turbulence conditions. The performance is examined based on the average channel capacity (ACC), which is expressed in terms of average spectral efficiency (ASE) parameters while changing the different parameters of the model. The mathematical formulas of ACC for atmospheric turbulence cases are calculated and discussed the influence of turbulence strength, the different number of relay stations, misalignment effects, and different MIMO configurations.
Performance evaluation of LoRa based sensor node and gateway architecture for oil pipeline management
Authors: Lakshmi Narayana Chavala , Singh Rajesh , Gehlot Anita
Published: November 2021

These days, the oil industrial industry is leaning toward employing smart field improvements to streamline various activities in the midstream area. Oil transportation over large distances via pipelines has a cheap cost and high efficiency in this sector. If pipelines are not properly maintained, they may fail, potentially causing catastrophic, long-term, and irreversible consequences on both natural and human conditions. Low power wide area networks (LPWANs) are without a doubt one of the domains that cause the most from industrial fields when it comes to realizing the vision of the internet of things (IoT). Long-range (LoRa) is an emerging LPWAN technology that is particularly useful for transmitting data over long distances. The goal of this work is to offer a methodology for managing oil pipelines over long distances utilizing the LoRa communication protocol and the installation of sensor nodes and LoRa gateways along the pipeline. We also used the optimized network engineering tools (OPNET) simulator to examine various simulation findings of LoRa performance.
A 15-Gbps BiCMOS XNOR gate for fast recognition of COVID-19 in binarized neural networks
Authors: W. Marar Rosana , W. Marar Hazem
Published: November 2021

The COVID-19 pandemic is spreading around the world causing more than 177 million cases and over 3.8 million deaths according to the European Centre for Disease Prevention and Control. The virus has devastating effects on economies, health, and well-being of worldwide population. Due to the high increase in daily cases, the available number of COVID-19 test kits in under-developed countries is scarce. Hence, it is vital to implement an effective screening method of patients using chest radiography since the equipment already exists. With the presence of automatic detection systems, any abnormalities in chest radiography that characterizes COVID-19 can be identified. Several artificial-intelligence algorithms have been proposed to detect the virus. However, neural networks training is considered to be time-consuming. Since computations in training neural networks are spent on floating-point multiplications, high computational power is required. Multipliers consume the most space and power among all arithmetic operators in deep neural networks. This paper proposes a 15 Gbps high-speed bipolar-complementary-metal-oxide-semiconductor (BiCMOS) exclusive-nor (XNOR) gate to replace multipliers in binarized neural networks. The proposed gate can be implemented on BiCMOS-based field-programmable gate arrays (FPGAs). This will significantly improve the response time in identifying chest abnormalities in CT scans and X-rays.
Anomaly event detection and localization of video clips using global and local outliers
Authors: Mahmood Sawsen Abdulhadi , Abid Azal Monshed , Lafta Sadeq H.
Published: November 2021

The automatic detection of anomaly events in video sequence has become a critical issue and essential demand for the extensive deployment of computer vision systems such as video surveillance applications. An anomaly event in video can be denoted as outlier behavior within video frames which formulated by a deviation from the stable scene. In this paper, an anomaly event detection and localization method in video sequence is presented including multilevel strategy as temporal frames differences estimation, modelling of normal and abnormal behavior using regression model and finally density–based clustering to detect the outliers (abnormal event) at clips level. Hence, outlier score is obtained at the segment or clip level along video frames sequences. The proposed method seplits video frames into nonoverlapped clips using global outlier detection process. Afterward, at each clip, the local outliers are determined based on density of each clip. Extensive experiments were conducted upon two public video datasets which include dense and scattered outliers along video sequence. The experiments were performed on two common public datasets (Avenue) and University of California, San Diego (UCSD). The experimental results exhibited that the proposed method detects well outlier frames at clip level with lower computational complexity comparing to the state-of-the-art methods.
Morphological characteristics of X-ray thorax images of COVID-19 patients using the Bradley thresholding segmentation
Authors: Supriyanti Retno , Alqaaf Muhammad , Ramadhani Yogi , Widodo Haris B.
Published: November 2021

The coronavirus disease 2019 (COVID-19) pandemic has made test screening much needed. Currently, the most commonly used is the swab type. Although in fact, there is also a screening method with chest radiology. The purpose of this study is to develop a COVID-19 early detection system based on X-ray images of the patient's thorax in the form of a computer-aided diagnosis. This case is based on the fact that X-ray modalities are available in several health care centers in Indonesia, compared to other modalities such as computed tomography (CT) scan or magnetic resonance imaging (MRI). In this paper, we emphasize the X-ray thorax image segmentation process to explore the morphological information of the thorax. We use the Bradley thresholding segmentation method. The results obtained are promising to be further developed with a performance percentage of 73.33% for the thorax for COVID-19 patients and 54% for the thorax for normal patients.
A simple, effective distance and density based outlier detection algorithm
Authors: A. Sajidha S. , Agarwal Udai , P. Pruthviraj R. , Agarwal Sparsh , M. Nisha V. , Tyagi Amit Kumar
Published: November 2021

Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lot of attention in the data processing community. But, they inordinately affect the quality of the results obtained in case of popular clustering algorithms during the process of finding an optimal solution. In this work, we propose a novel method to classify the data points with grouping characteristics as either an outlier or not. We use both distance and density of a particular data point with respect to the rest of the data points for this process. Distances are used to find the points at the extremities while the densities are used to identify the data points at the sparsest spaces. Further, every data model has to take into account the aspect of generalization in order to work robustly even in out of the box situations. Hence, our approach provides a generalization aspect to the model. The accuracy of the proposed work is measured using area under curve (AUC) was found the highest for cardioto data set -AUC value-0.90 and second highest AUC value was obtained for Spambase data set -0.52 and several other datasets are used to demonstrate the usage of the model proposed.
Implementation of feature extraction and deep learning-based ensemble classifier for interference mitigation in radar signals
Authors: Indira N. Durga , Rao M. Venu Gopala
Published: November 2021

In automotive vehicles, radar is the one of the component for autonomous driving, used for target detection and long-range sensing. Whereas interference exists in signals, noise increases and it effects severely while detecting target objects. For these reasons, various interference mitigation techniques are implemented in this paper. By using these mitigation techniques interference and noise are reduced and original signals are reconstructed. In this paper, we proposed a method to mitigate interference in signal using deep learning. The proposed method provides the best and accurate performance in relate to the various interference conditions and gives better accuracy compared with other existing methods.
An ear recognition system based on local wavelet subband energy distribution
Authors: Fadhil Ruaa Isam , George Loay E.
Published: November 2021

The outer ear features have been used for many years in forensic science of recognition. Human ear is a valuable information provenance of data for individual identification/authentication. Ear meets biometric characteristic (universality, distinctiveness, permanence and collectability). Biometric system depending on ear image facing two major challenges; the first one is the localization of human ear area in given profile face image, and the second one is the selection of proper features to distinguish between individuals. In this work, we propose an alogorithm for ear recognition based on the local spatial energy distribution of wavelet sub-bands, because of wavelet transform has the ability to analyze the local feature of 2-D image by determining where the low frequency and high frequency areas are and it provides full description of the spatial distribution of the ear image. Nearest classifier are used to make a recognition decision in matching stage. The system was tested over a public database consist of 493 images. The attained recognition rate was (95.28%) and the achieved minimum equal error rate (EER) is 0.02%.
Electromagnetic nonlinear parametric study of the SynRM using FEM method
Authors: Djamel Benessalah , Hamza Houassine , Kabache Nadir , Djeloul Moussaoui
Published: November 2021

The interest in synchronous reluctance machine (SynRM) does not stop increasing in recent decades; this is encouraged by their numerous advantages. This paper presents a nonlinear parametric study of the SynRM using finite element method (FEM) method. After a brief introduction and a description of the basic principles of SynRM an investigation and an evaluation of the effects of some influential parameters’ variables of the machine on the torque and magnetic losses is highlighted. The SynRM is created using ANSYS Maxwell software, using 2D FEM. The analyses are performed in the ANSYS Maxwell. The influence of the thickness of the air gap, the opening angle of the rotor, the width and the height of the stator tooth are listed and discussed. The obtained results reveals that the opening angle of the rotor and the air gap produces a large effect over the torque of the SynRM. In order to validate, the finite element model of the studied machine, experimental tests were carried out on designed machine such as the measurement of the synchronous inductance, the torque and the different losses. The experimental results are in agreement with those obtained by FEM.
Diagnosis of rotor faults by fast Fourier transform using an auxiliary winding voltage
Authors: Jelbaoui Yakout Khadouj , El Menzhi Lamiaa , Saad Abdallah
Published: November 2021

Condition monitoring and on-line detection have attracted several researchers in order to carry out an efficient diagnosis of machine failures. Therefore, the detection in early stage avoids system breakdown and reduce the maintenance cost. This paper presents a new diagnosic approach to detect the broken bars and broken end ring faults for a squirrel cage induction machine based on the fast Fourier transform analysis applied on a new signature as the voltage of an auxiliary winding. The auxiliary winding is a small coil inserted between two of the stator phases, the explicit expression of its voltage is presented. The signal is monitored in six faults cases under a different load level, the emergence of the fault frequencies changes for each kind of failure. The successfully simulation results obtained show the effectiveness of this approach.
High gain multiphase boost converter based-on capacitor clamping structure
Authors: Fares Oday Saad , Hussen Jasim Farhood
Published: November 2021

In the last few years, the non-isolated dc converters involving high voltage gain with adequate performance are becoming quite popular in industrial applications. This is resulting in high voltage and current stress on the power device (switches and diodes), as well as a limited output voltage with a high duty cycle. This paper proposes a multi-phase non-isolated boost converter that uses capacitor clamping to increase output voltage while reducing stress across the power device. There are two stages in the proposed converter (first stage is three inductors and three switches and the second stage is clamper circuit of three capacitors and three diodes). The proposed converter is high voltage gain, with low voltage stress through switches transistors. To justify the theoretical analysis, the concept was validated through mathematical analysis and by simulation using MATLAB/SIMULINK. The results carried out the results permit the converter behavior and performance to be accurately.
Automation of the stone materials dosing process, controlled by variable frequency drives
Authors: Morales-Romero Guillermo , Trinidad-Loli Nicéforo , Quispe-Andía Adrián , Caycho-Salas Beatriz , Quispe-Guía Shirley , Palacios-Huaraca Carlos , Chamorro-Atalaya Omar
Published: November 2021

The objective of this article is to determine to what extent the automation of the stone materials dosing process, controlled by sequential drive of frequency variators, contributes to improving the productivity of a company dedicated to the production of asphalt in Peru for which, initially, the characteristics of the procedure that will lead to achieving the automation will be described. The results will then be displayed with respect to the indicators used to compare productivity before and after automation. The automation will be done by means of the logo 230RE controller, which will be connected to three frequency inverters, the programming development will be through the logo soft comford V8 software, for the sequential actuation, timers with connection delay will be used. Applying the automation, it is possible to improve the annual efficiency by an average of 58.30%, this is reflected in the monthly decrease in production time by 13.92%, in turn increasing the amount of stone material produced by an average of 43.77%. Likewise, it is possible to significantly reduce the production loss capacity by an annual average of 93.99%.
A novel hybrid face recognition framework based on a low-resolution camera for biometric applications
Authors: R. Vijaya Kumar H. , Mathivanan M.
Published: November 2021

In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.
Exploiting outage performance in device-to-device for user grouping
Authors: Do Dinh-Thuan , Le Chi-Bao
Published: November 2021

The spectrum efficiency and massive connections are joint designed in new form of device-to-device for user grouping. A pair of users is implemented with nonorthogonal multiple access (NOMA) systems. Although NOMA benefits to such system in term of the serving users, device to device (D2D) faces the interference from normal cellular users (CUE). In particular, we derive exact formulas of outage probability to show system performance. In this article, we compare two schemes to find relevant scheme to implement in practice. The frame structure is designed with two timeslot related to uplink and downlink between the base station and D2D users. We confirm the better scheme in numerical result by considering the impacts of many parameters on outage performance.
Compact MIMO antenna using dual-band for fifth-generation mobile communication system
Authors: Mishra Debani Prasad , Rout Kshirod Kumar , Salkuti Surender Reddy
Published: November 2021

This paper presents the design of a multiple-input and multiple-output (MIMO) antenna for a fifth-generation (5G) smartphone that will work in dual-band. The antenna proposed in this work operates at 2 frequency ranges, i.e., (3300-3600) MHz and (4800-5000) MHz. The antenna design consists of four antennas that are placed perpendicular to the edge of the system and this makes it different from the traditional 5G antennas. The area of each antenna on the side frames is (3.9×17 mm), and hence can be used in ultra-thin smartphones for 5G applications. The reflection coefficient obtained in the simulations is less than -6 dB for the required band, which suggests that the required impedance matching is obtained. The antenna proposed is designed by using central time zone (CST) microwave studio.
Sidelobe level minimization for uniform circular smart antenna array using cultural algorithm
Authors: Mohsin Asma Issa , Daghal Asaad S. , Sallomi Adheed Hasan
Published: November 2021

Cultural algorithm (CA) is a new evolutionary program inspired by sociology and archaeology theories that assisting formulating cultural evaluation. Its use to solve optimization problems. This paper analyzed the beamforming of a uniform circular antenna array (UCAA) via using the CA algorithm. The sidelobe level (SLL) is minimized by adjusting the appropriate weight for each element. In addition, the optimal beam pattern is achieved by using CA for UCAA, which means that the main beam is steering to the desired user, while the nulls represent the interference signals. The excitation amplitude is supposed to be constant while the elements are assumed isotropic. The circular array number elements and the interspacing distance between them are setting as optimization parameters. The simulation results show that the CA rationally reacts to the changing environments, and it is valuable for SLL reduction. A −25 dB of relative SLL is achieved under beam scanning (0º) and (15º), respectively.