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Half-metallic porphyrin-based molecular junctions for spintronic applications

Physical Review B 2021年第23期104卷 235435-235435页

作者： Azar Ostovan Nick Papior S. Shahab Naghavi Department of Physical and Computational Chemistry Shahid Beheshti University *** Tehran Iran DTU Computing Center Department of Applied Mathematics and Computer Science Technical University of Denmark DK-2800 Kongens Lyngby Denmark

Single molecular magnets (SMMs) have become promising paradigms to develop novel spintronics for futuristic information technologies such as high-density information and quantum computing. The efficiency and characteristics of SMM devices are determined by the intrinsic nature of the molecular magnets placed in the spin transport pathway. In this work, to understand the role of the central magnetic ion on the performance of SMM devices, we screen the spin-conductance properties of whole 3d and 4d metalloporphyrins using the nonequilibrium Green's function formalism in conjunction with density functional theory. Our results show that the investigated SMMs according to spintronic conductance behavior can be categorized into three groups: type I non-spin-polarized, type II′ minor spin-polarized current, and type II″ major spin-polarized current devices. Type-II′ and type-II″ molecular systems show perfect spin filtering and spin-dependent negative differential resistance. The optimal energy alignment of spin-polarized molecular orbitals with gold electrodes results in one-channel spin transport (minor for type II′ and major for type II″). Thus type-II junctions are half-metal. The type-II″ junctions also show a voltage-induced spin switchability at low bias voltages. In this regard, type-II molecular systems are promising candidates for a low-power consumption spin filter, spin switch, memory, to name just a few. Our results highlight the practical applications of metalloporphyrin for the development of multipurpose miniature spintronic devices.

关键词： Ballistic transport Electrical conductivity Electronic structure Electronic structure of atoms & molecules First-principles calculations Magnetism Molecular magnetism Quantum transport Solid-state chemistry Spintronics Transport phenomena

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Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP

arXiv

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arXiv 2024年

作者： Ismail, Muhammad Abbas, Farkhanda Shah, Shahid Munir Aljawarneh, Mahmoud Dhomeja, Lachhman Das Abbas, Fazila Shoaib, Muhammad Alrefaei, Abdulwahed Fahad Albeshr, Mohammed Fahad Department of Computer Science Karakoram International University Gilgit15100 Pakistan School of Computer Science China University of Geosciences Wuhan430074 China Department of Computing Faculty of Engineering Science and Technology Hamdard University Karachi74600 Pakistan Faculty of Information Technology Applied Science Private University Amman11937 Jordan Department of Information Technology Faculty of Engineering and Technology University of Sindh Jamshoro76080 Pakistan Institute of Soil and Environmental Sciences University of Agriculture Faisalabad Faisalabad38000 Pakistan State Key Laboratory of Hydraulic Engineering Simulation and Safety School of Civil Engineering Tianjin University Tianjin300072 China Department of Zoology College of Science King Saud University Riyadh11451 Saudi Arabia

Environmental monitoring and predictive modeling of Water Quality Index (WQI) through the assessment of the water quality is an important area of research. In order to predict WQI this study utilizes four popular Deep Learning models i.e. Artificial Neural Network (ANN), Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). Based on the Area Under the Operating Characteristic Curve (AUC), the per- formance of the employed models is evaluated and the results are compared. Comparative analysis showed that TCN achieved the highest AUC score i.e. 0.94 as compared to the other employed models i.e. MLP: 0.94,’LSTM’: 0.77, and’ANN’: 0.93. This comparative study provides insights into the suitability of various Deep Learning models for predicting WQI, contributing to advancements in environmental monitoring and management practices. © 2024, CC BY.

关键词： Environmental monitoring

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Next Generation Image Watermarking via Combined DWT-SVD Technique

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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Cyber Resilience (ICCR), International Conference on

作者： Qais Al-Na'amneh Ammar Almomani Ahmad Nasayreh Khalid M.O. Nahar Hasan Gharaibeh Rabia Emhamed Al Mamlook Mohammad Alauthman Department of Cyber Security Applied Science Private University Amman Jordan School of Computing Skyline University College Sharjah United Arab Emirates I. T. department AlHuson University College AlBalqa Applied University Irbid Jordan Department of Computer Sciences Yarmouk University Irbid Jordan Faculty of Information Technology Computer Sciences Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Dep.of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Security Faculty of Information Technology University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image.

关键词： Resistance PSNR Watermarking Transforms Streaming media Robustness Real-time systems

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DH-GAN: A PHYSICS-DRIVEN UNTRAINED GENERATIVE ADVERSARIAL NETWORK for 3D MICROSCOPIC IMAGING USING DIGITAL HOLOGRAPHY

arXiv

引用

arXiv 2022年

作者： Chen, Xiwen Wang, Hao Razi, Abolfazl Kozicki, Michael Mann, Christopher School of Computing Clemson University United States School of Electrical Computer and Energy Engineering Arizona State University United States Department of Applied Physics and Materials Science Northern Arizona University United States

Digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms. The object's 3D shape can be obtained by numerical analysis of the captured holograms and recovering the incurred phase. Recently, deep learning (DL) methods have been used for more accurate holographic processing. However, most supervised methods require large datasets to train the model, which is rarely available in most DH applications due to the scarcity of samples or privacy concerns. A few one-shot DL-based recovery methods exist with no reliance on large datasets of paired images. Still, most of these methods often neglect the underlying physics law that governs wave propagation. These methods offer a black-box operation, which is not explainable, generalizable, and transferrable to other samples and applications. In this work, we propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality while using a generative network as a function approximator to model the inverse of hologram formation. We impose smoothness on the background part of the recovered image using a progressive masking module powered by simulated annealing to enhance the reconstruction quality. The proposed method is one of its kind that exhibits high transferability to similar samples, which facilitates its fast deployment in time-sensitive applications without the need for retraining the network. The results show a considerable improvement to competitor methods in reconstruction quality (about 5 dB PSNR gain) and robustness to noise (about 50% reduction in PSNR vs noise increase rate). © 2022, CC BY.

关键词： Holograms

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Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management

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Measurement: Sensors 2023年 30卷

作者： Ahamed, Bazeer Sellamuthu, Suseela Karri, Purushotam Naidu Srinivas, Inumarthi V. Mohammed Zabeeulla, A.N. Ashok Kumar, M. Department of IT University of Technology And Applied Sciences Al Musanna Oman Senior Grade School of Computing Sciences and Engineering Vellore Institute of Technology TamilNadu Chennai India Department of CSE Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam535546 India Mumbai400077 India Bangalore India Department of Computer Science and Software Engineering Skyline University Nigeria.Kano Nigeria

Advances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applications for remote health monitoring generate massive amounts of data in a short period. Internet of Things (IoT) and cloud systems, resulting in unprocessed and inadequate data for end-users. Since cloud technology is a dispersed intermediary layer between the edge network and the cloud infrastructure, the delay is significantly decreased, and reliable communications can be established using cloud computing on an IoT-enabled healthcare device. This study presents an Energy Efficient Healthcare Data Management Method (EE-HDMM), which enhances health monitoring using an IoT-assisted wearable sensor infrastructure. The difficulty of constrained sources and the energy consumption is minimized by proposing the optimum energy resource allocation method. Wearable sensor technology supported by cloud computing is the ideal and dependable platform for powerful life-critical systems that are unlikely to have communication delays. Compared to conventional techniques, the suggested EE-HDMM has a high precision rate of 97%, a sensitivity rate of 94%, and a system efficiency of 96%. © 2023

关键词： Internet of things

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SUBCELL LIMITING STRATEGIES FOR DISCONTINUOUS GALERKIN SPECTRAL ELEMENT METHODS

arXiv

引用

arXiv 2022年

作者： Rueda-Ramírez, Andrés M. Pazner, Will Gassner, Gregor J. Department of Mathematics and Computer Science University of Cologne Cologne50931 Germany Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Center for Data and Simulation Science University of Cologne Cologne50931 Germany

We present a general family of subcell limiting strategies to construct robust high-order accurate nodal discontinuous Galerkin (DG) schemes. The main strategy is to construct compatible low order finite volume (FV) type discretizations that allow for convex blending with the high-order variant with the goal of guaranteeing additional properties, such as bounds on physical quantities and/or guaranteed entropy dissipation. For an implementation of this main strategy, four main ingredients are identified that may be combined in a flexible manner: (i) a nodal high-order DG method on Legendre-Gauss-Lobatto nodes, (ii) a compatible robust subcell FV scheme, (iii) a convex combination strategy for the two schemes, which can be element-wise or subcell-wise, and (iv) a strategy to compute the convex blending factors, which can be either based on heuristic troubled-cell indicators, or using ideas from flux-corrected transport methods. By carefully designing the metric terms of the subcell FV method, the resulting methods can be used on unstructured curvilinear meshes, are locally conservative, can handle strong shocks efficiently while directly guaranteeing physical bounds on quantities such as density, pressure or entropy. We further show that it is possible to choose the four ingredients to recover existing methods such as a provably entropy dissipative subcell shock-capturing approach or a sparse invariant domain preserving approach. We test the versatility of the presented strategies and mix and match the four ingredients to solve challenging simulation setups, such as the KPP problem (a hyperbolic conservation law with non-convex flux function), turbulent and hypersonic Euler simulations, and MHD problems featuring shocks and turbulence. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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A Multi-Module Spatial Semantic Network with Gradient Boosting for Efficient Crop Classification

A Multi-Module Spatial Semantic Network with Gradient Boosti...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Saif O. Husain Meghana A Rajireddy Soujanya G Sunil Vijaya Bhaskar Reddy Muvva Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences –Shinas Shinas Sultanate of Oman

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

For the past few years, accurate crop classification is critical for efficient agricultural management and monitoring. While traditional classification techniques have leveraged spectral indices, they often struggle to fully capture the complex and diverse multispectral features present in agricultural landscapes. This challenge is particularly evident when differentiating crops with similar spectral signatures, leading to misclassification and reduced accuracy in traditional models. To address this problem, a hybrid approach that combines a deep learning model namely Multi-module Spatial Semantic Network (MSSNet) with the Gradient Boosting Classifier (GBC) is proposed. The dataset utilized in this research is sentinel-2, where the median filter is used to remove noise at the preprocessing stage. Then, MSSNet architecture is applied in feature extraction where pre-trained residual network (ResNet50), embedded receptive field module (RFB) and skip connection are incorporated to extract multi-spectral and spatial features. Finally, the extracted features are fed into a GBC, which is suitable for the robust performance in pixel mapping. This MSSNet+GBC combination leverages the strengths and offers improved accuracy in distinguishing between crop types and conditions. The experimental results demonstrate that MSSNet+GBC outperforms conventional methods such as 3D-Convolutional Neural Network (CNN) with higher accuracy of 98.67% using a sentinel-2 dataset for precise crop classification.

关键词： Deep learning Accuracy Semantics Noise Transfer learning Soil moisture Crops Feature extraction Boosting Residual neural networks

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Evaluating Security of MQTT Protocol in Internet of Things

Evaluating Security of MQTT Protocol in Internet of Things

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Ayman Al-Ani Wong Kang Shen Ahmed K. Al-Ani Shams A. Laghari Omar E. Elejla Cybersecurity Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah Malaysia School of Computing and Data Science Xiamen University Malaysia Sepang Selangor Malaysia School of Engineering Technology and Applied Science Centennial College Toronto Ontario Canada National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia (USM) Gelugor Penang Malaysia Department of Computer Science Al-Aqsa University Gaza Palestine

The Internet of Things (IoT) has revolutionized the way people interact, communicate, and perform daily activities in various domains ranging from households to industries and cities. MQTT is one of the commonly adopted protocols for implementing IoT. However, IoT systems that are connected through MQTT are susceptible to security breaches as MQTT was not originally designed with security as a priority. The credentials and messages transmitted in plaintext by default, thereby compromising data confidentiality and integrity. This study presents a comprehensive analysis of the MQTT protocol, including experimentation on an MQTT system using various cryptographic implementations, such as AES-CBC, RSA, and ECC AES Hybrid Scheme, to assess the processing time and message size. The findings indicate that payload encryption increases processing time and message bytes. Among the cryptographic implementations, RSA incurs the highest processing time, followed by ECC AES Hybrid Scheme and AES- CBC. Furthermore, the study demonstrates the effectiveness of attack prevention between standard MQTT and secured MQTT implementations by simulating various IoT attacks, such as black-box penetration attack, identity spoofing, DoS attack, and MITM attack. The results and subsequent discussion provide insights that answer the research question, revealing the cryptographic algorithms that result in the most overhead on the standard MQTT implementation and their capacity to resist common attacks.

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Blockchain Solution For SMEs Cybersecurity Threats In E-Commerce

Blockchain Solution For SMEs Cybersecurity Threats In E-Comm...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Firas H. Zawaideh Waheeb Abu-Ulbeh Salameh A. Mjlae Yousef A. Baker El-Ebiary Yazeed Al Moaiad Sunanda Das Department of Cyber Security Faculty of Science and Informaiton Technology Irbid National University Irbid Jordan Cybersecurity Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine CS Department Prince Abdullah bin Ghazi Faculty of Information Technology Al-Balqa Applied University (BAU) Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Computer and Information Technology MEDIU Malaysia School of Computer Science and Engineering Jain University Bangalore India

The rapid growth of electronic commerce (e-commerce) has revolutionized the way Small and Medium-sized Enterprises (SMEs) conduct business, enabling them to reach global markets and unlock new growth opportunities. However, this digital transformation has also exposed SMEs to an escalating array of cybersecurity threats that could compromise their data, financial integrity, and reputation. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to enhance the security and resilience of e-commerce operations for SMEs. This research paper aims to investigate the current state of cybersecurity preparedness among SMEs engaged in e-commerce and identify the prevalent threats they face in the digital landscape. To achieve this goal, the study incorporates systematic literature review (SLR) method to examine the best technological solution to address the cybersecurity issues faced by e-commerce organizations in this paper of SMEs operating in e-commerce to gather data on their security practices, incident history, and perceptions of cybersecurity risks. In the context of this research, blockchain technology will be explored as a potential mechanism to enhance the security of e-commerce operations for SMEs. The inherent features of blockchain, such as decentralization, transparency, and immutability, could help protect sensitive data and thwart cyberattacks. By exploring the integration of blockchain into e-commerce systems, the research aims to shed light on the potential benefits and challenges associated with its adoption by SMEs. The research findings will not only shed light on the prevailing cybersecurity practices within SMEs and highlight their vulnerabilities but will also explore how blockchain technology can be leveraged to address these challenges. By understanding these patterns and exploring the feasibility of blockchain integration, the research aims to provide comprehensive recommendations for enhancing cy

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Segmentation and Classification of Gastric Cancer from Endoscopic Image Dataset with the Aid of Artificial Intelligence

Segmentation and Classification of Gastric Cancer from Endos...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Cuddapah Anitha Vemu Srinivas Rao Ramyadevi. R Maya. R Subhasish Debroy Mahaveerakannan R School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh Department of Computer Science & Engineering Saveetha Engineering College Department of Electrical & Electronics Engineering Chennai Institute of Technology & Applied Research Dr. B.C. Roy Engineering College Durgapur West Bengal Department of Computer Science and Engineering Saveetha School of Engineering SIMETs Chennai Tamil Nadu India

In order to detect early stomach malignancies, upper GI endoscopy is commonly used. An object detection model, a form of deep learning, was projected as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. Yet there were difficulties in reducing false positives in the results that were detected. Thus, this research proposes an automatic classification method for stomach cancer classification using a (CNN) that has been pre-trained. To classify cancer in endoscopic pictures automatically, our method is superior to those that rely on traditional, manual features. Thirteen convolutional layers with small 33 size kernels and three fully linked layers make up the suggested model. We use the learning approach, which involves pre-training the weight of fine-tuning the weight of layers with the Slime Mould Algorithm, to deal with the data scarcity (SMA). Experiments employing 1208 photos from healthy subjects and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross validation approach. These findings suggest the projected strategy will be effective for automating the diagnosis of early stomach cancer in endoscopic pictures.

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