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Reconfigurable physical unclonable cryptographic primitives based on current-induced nanomagnets switching

Science China(Information Sciences) 2022年第2期65卷 188-196页

作者： Shuai ZHANG Jian ZHANG Shihao LI Yaoyuan WANG Zhenjiang CHEN jeongmin HONG Long YOU Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and Technology

Hardware security primitives that preserve secrets are playing a crucial role in the Internet-ofThings(Io T) era. Existing physical unclonable function(PUF) instantiations, exploiting static randomness,generate challenge-response pairings(CRPs) to produce unique security keys that can be used to authenticate devices linked to the Io T. Reconfigurable PUFs(RPUFs) with dynamically refreshable CRPs can enhance the security and robustness of conventional PUFs. The in-plane current-driven perpendicular polarized nanomagnet switching via spin-orbit torque(SOT) possesses great potential for application to memory and logic, as the write-current path is separate from the read-current path, which naturally resolves the write-read interference. However, the stochastic switching of perpendicular magnetization, without an additional symmetry-breaking field, would significantly hinder the technological viability of commercial implementations. Here, we report an initialization-free physical RPUF implemented by SOT-induced stochastic switching of perpendicularly magnetized Ta/Co Fe B/Mg O nanodevices. Using a 15×15 nanomagnet array, we experimentally demonstrate a security primitive that offers a near-ideal 50% uniqueness over 100 reconfiguration cycles, as well as a low correlation coefficient between every two reconfiguration cycles. Our results show that current-induced nanomagnets switching paves the way for developing highly reliable and energy-efficient reconfigurable cryptographic primitives with a smaller footprint.

关键词： reconfigurable physical unclonable function spin-orbit torque cryptographic primitive spintronics nanomagnet

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Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

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Computers, Materials & Continua 2023年第5期75卷 3275-3289页

作者： Ali Raza Samia Allaoua Chelloug Mohammed Hamad Alatiyyah Ahmad Jalal jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for ***,human detection and visualization have become essential inventions in the field of computer ***,developing a surveillance system with multiple object recognition and tracking,especially in low light and night-time,is still ***,we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at *** particular,we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared(IR)images using machine learning and tracking them using particle ***,a random forest classifier is adopted for image segmentation to identify pedestrians in an *** result of detection is investigated by particle filter to solve pedestrian *** the extensive experiment,our system shows 93%segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof ***,the system achieved a detection accuracy of 90%usingmultiple templatematching techniques and 81%accuracy for pedestrian ***,our system can identify that the detected object is a ***,our system provided the best results compared to the state-ofart systems,which proves the effectiveness of the techniques used for image segmentation,classification,and *** presented method is applicable for human detection/tracking,crowd analysis,and monitoring pedestrians in IR video surveillance.

关键词： Pedestrian detection machine learning segmentation tracking verification

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A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

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Computers, Materials & Continua 2023年第4期75卷 959-976页

作者： MZeeshan Sarwar Mohammed Hamad Alatiyyah Ahmad Jalal Mohammad Shorfuzzaman Nawal Alsufyani jeongmin Park Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given *** cues can improve the quality of object detection and *** main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D *** proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not ***,the proposed RGB-D-based system uses depth cues for segmentation with the help of *** it extracts multiple features from the segmented *** selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting *** achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 ***,their results are compared and the proposed model with CNN outperforms other state-of-the-art *** proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

关键词： Artificial intelligence convolutional neural network depth images interactive-object detection machine learning

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An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

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Computers, Materials & Continua 2022年第8期72卷 2597-2615页

作者： Yazeed Yasin Ghadi Nida Khalid Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or ***,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of *** propose a robust system comprising both RGB(red,green,blue)and depth *** particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency *** track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector ***,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for *** are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity ***,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI *** results proved the validity of the proposed system.

关键词： Artificial neural network human-human interaction humanobject interaction k-ary tree hashing machine learning

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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

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Computers, Materials & Continua 2022年第12期73卷 4763-4777页

作者： Mir Mushhood Afsar Shizza Saqib Yazeed Yasin Ghadi Suliman A.Alsuhibany Ahmad Jalal jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Virtual reality is an emerging field in the whole *** problem faced by people today is that they are more indulged in indoor technology rather than outdoor ***,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person *** system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the *** track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the ***,the sensor data is sent to a machine learning model,which detects the game played by the *** detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human *** main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting *** proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational *** experimentation proved the validity of the proposed system.

关键词： Artificial neural networks bluetooth connection inertial sensors machine learning virtual reality exergaming

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Exploiting Human Pose and Scene Information for Interaction Detection

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Computers, Materials & Continua 2023年第3期74卷 5853-5870页

作者： Manahil Waheed Samia Allaoua Chelloug Mohammad Shorfuzzaman Abdulmajeed Alsufyani Ahmad Jalal Khaled Alnowaiser jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image *** third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

关键词： Artificial intelligence daily activities human interactions human pose information image foresting transform scene feature information

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Hemodynamic instability following intravenous dexmedetomidine infusion for sedation under brachial plexus block: Two case reports

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World Journal of Clinical Cases 2023年第30期11卷 7469-7474页

作者： Ye Sull Kim Chanhong Lee jeongmin Oh Seonhwa Nam A Ram Doo Department of Anesthesiology and Pain Medicine Jeonbuk National University HospitalJeonju 54907South Korea Department of Anesthesiology and Pain Medicine Jeonbuk National University Medical School and HospitalJeonju 54907South Korea Department of Anesthesiology and Pain Medicine Research Institute of Clinical Medicine-Biomedical Research Institute of Jeonbuk National University HospitalJeonju 54907South Korea

BACKGROUND Dexmedetomidine(DMED)is frequently used as a sedative in several medical *** benefits of DMED include enhanced quality of regional anesthesia,prolonged analgesia,and postoperative opioid-sparing when administered intravenously or perineurally in combination with regional *** hemodynamic complications,such as profound bradycardia and hypotension,can occur after DMED administration in critically ill patients or overdosage;however,there are few reports of complications with DMED administration following brachial plexus block(BPB).CASE SUMMARY We present two cases of hemodynamic instability that occurred following the initial loading of DMED under supraclavicular BPB.A healthy 29-year-old man without any medical history showed profound bradycardia after receiving a loading dose of DMED 0.9μg/kg for 9 *** administration was promptly stopped,and after receiving a second dose of atropine,the heart rate recovered.A 62-year-old woman with a history of cardiomyopathy became hypotensive abruptly,requiring the administration of inotrope and vasopressors after receiving a reduced loading dose of 0.5μg/kg for 10 *** of the recommended loading dose of DMED was administered due to the underlying heart ***-sed blood pressure was maintained despite the intravenous administration of *** continuous infusion of dopamine and norepinephrine,the vital signs were maintained within normal *** and vasopressor support was required for over 6 h after the initial loading dose of *** DMED administration following BPB could trigger hemodynamic instability in patients with decreased cardiac function as well as in healthy individuals.

关键词： Dexmedetomidine Brachial plexus block Profound bradycardia Complication Hypotension Instability Case report

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CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

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Computers, Materials & Continua 2022年第12期73卷 4657-4675页

作者： Adnan Ahmed Rafique Yazeed Yasin Ghadi Suliman AAlsuhibany Samia Allaoua Chelloug Ahmad Jalal jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene *** sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist *** visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene *** this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an ***,after acquisition and preprocessing,the image is segmented by using ***,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are *** the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion ***,to select theminimal set of features,genetic algorithm-based feature selection is *** order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is *** objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation ***,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the *** experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.

关键词： Convolutional neural network decision tree feature fusion neurofuzzy system

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Self-Care Assessment for Daily Living Using Machine Learning Mechanism

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Computers, Materials & Continua 2022年第7期72卷 1747-1764页

作者： Mouazma Batool Yazeed Yasin Ghadi Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer *** and optical sensors are widely used to assess the daily living activities in healthy people and people with certain *** conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity *** this paper,an RGB-D-based ADL recognition system has been ***,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a *** on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)***,random forest classifier has been used to classify activities into different *** n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.

关键词： Angular geometric features decision tree classifier human activity recognition probability based incremental learning ridge detection

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Pedestrian Physical Education Training Over Visualization Tool

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Computers, Materials & Continua 2022年第11期73卷 2389-2405页

作者： Tamara al Shloul Israr Akhter Suliman A.Alsuhibany Yazeed Yasin Ghadi Ahmad Jalal jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

E-learning approaches are one of the most important learning platforms for the learner through electronic *** study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and *** of them are currently working on this domain to fulfill the requirements of industry and the *** this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural *** input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is *** flow features are extracted to find more accurate ***,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior *** proposed method shows 84.50%of mean accuracy and a 15.50%of error ***,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).

关键词： Artificial intelligence behavior mining convolution neural network(CNN) deep flow e-learning environment

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