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نتیجه جستجو - Challenges

تعداد مقالات یافته شده: 1441
ردیف عنوان نوع
1 Intelligent authentication of 5G healthcare devices: A survey
احراز هویت هوشمند دستگاه های مراقبت بهداشتی 5G: یک مرور-2022
The dynamic nature of wireless links and the mobility of devices connected to the Internet of Things (IoT) over fifth-generation (5G) networks (IoT-5G), on the one hand, empowers pervasive healthcare applications. On the other hand, it allows eavesdroppers and other illegitimate actors to access secret information. Due to the poor time efficiency and high computational complexity of conventional cryptographic methods and the heterogeneous technologies used, it is easy to compromise the authentication of lightweight wearable and healthcare devices. Therefore, intelligent authentication, which relies on artificial intelligence (AI), and sufficient network resources are extremely important for securing healthcare devices connected to IoT- 5G. This survey considers intelligent authentication and includes a comprehensive overview of intelligent authentication mechanisms for securing IoT-5G devices deployed in the healthcare domain. First, it presents a detailed, thoughtful, and state-of-the-art review of IoT-5G, healthcare technologies, tools, applications, research trends, challenges, opportunities, and solutions. We selected 20 technical articles from those surveyed based on their strong overlaps with IoT, 5G, healthcare, device authentication, and AI. Second, IoT-5G device authentication, radiofrequency fingerprinting, and mutual authentication are reviewed, characterized, clustered, and classified. Third, the review envisions that AI can be used to integrate the attributes of the physical layer and 5G networks to empower intelligent healthcare devices. Moreover, methods for developing intelligent authentication models using AI are presented. Finally, the future outlook and recommendations are introduced for IoT-5G healthcare applications, and recommendations for further research are presented as well. The remarkable contributions and relevance of this survey may assist the research community in understanding the research gaps and the research opportunities relating to the intelligent authentication of IoT-5G healthcare devices.
keywords: اینترنت اشیا (IoT) | امنیت اینترنت اشیا | احراز هویت دستگاه | هوش مصنوعی | امنیت مراقبت های بهداشتی | شبکه های 5g | InternetofThings(IoT) | InternetofThingssecurity | Deviceauthentication | Artificialintelligence | Healthcaresecurity | 5Gnetworks
مقاله انگلیسی
2 Decentralization Using Quantum Blockchain: A Theoretical Analysis
تمرکززدایی با استفاده از بلاک چین کوانتومی: یک تحلیل نظری-2022
Blockchain technology has been prominent recently due to its applications in cryptocurrency. Numerous decentralized blockchain applications have been possible due to blockchains’ nature of distributed, secured, and peer-to-peer storage. One of its technical pillars is using public-key cryptography and hash functions, which promise a secure, pseudoanonymous, and distributed storage with nonrepudiation. This security is believed to be difficult to break with classical computational powers. However, recent advances in quantum computing have raised the possibility of breaking these algorithms with quantum computers, thus, threatening the blockchains’ security. Quantum-resistant blockchains are being proposed as alternatives to resolve this issue. Some propose to replace traditional cryptography with postquantum cryptography—others base their approaches on quantum computer networks or quantum internets. Nonetheless, a new security infrastructure (e.g., access control/authentication) must be established before any of these could happen. This article provides a theoretical analysis of the quantum blockchain technologies that could be used for decentralized identity authentication. We put together a conceptual design for a quantum blockchain identity framework and give a review of the technical evidence. We investigate its essential components and feasibility, effectiveness, and limitations. Even though it currently has various limitations and challenges, we believe a decentralized perspective of quantum applications is noteworthy and likely.
INDEX TERMS: Blockchains | consensus protocol | decentralized applications | identity management systems | quantum computing | quantum networks.
مقاله انگلیسی
3 IoT anomaly detection methods and applications: A survey
روش ها و کاربردهای تشخیص ناهنجاری اینترنت اشیا: یک مرور-2022
Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.
keywords: Anomaly detection | Internet of Things | IoT | Review | Survey | Applications
مقاله انگلیسی
4 Efficient Quantum Blockchain With a Consensus Mechanism QDPoS
بلاک چین کوانتومی کارآمد با مکانیزم اجماع QDPoS-2022
Quantum blockchain is expected to offer an alternative to classical blockchain to resist malicious attacks laughed by future quantum computers. Although a few quantum blockchain schemes have been constructed, their efficiency is low and unable to meet application requirements due to the fact that they lack of a suitable consensus mechanism. To tackle this issue, a consensus mechanism called quantum delegated proof of stake (QDPoS) is constructed by using quantum voting to provide fast decentralization for the quantum blockchain scheme at first. Then an efficient scheme is proposed for quantum blockchain based on QDPoS, where the classical information is initialized as a part of each single quantum state and these quantum states are entangled to form the chain. Compared with previous methods, the designed quantum blockchain scheme is more complete and carried out with higher efficiency, which greatly contributes to better adapting to the challenges of the quantum era.
Index Terms: Quantum blockchain | consensus mechanism | QDPoS | quantum voting | quantum entanglement.
مقاله انگلیسی
5 Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سخت‌افزار-نرم‌افزار روی تراشه مبتنی بر شبکه‌های عصبی عمیق برای کاربرد بینایی ماشین-2022
Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial intelligence (AI) in vision applications that make these systems intelligent and able to make decisions close to or similar to humans. In this context, the AI’s integration on embedded systems poses many challenges, given that its performance depends on data volume and quality they assimilate to learn and improve. This returns to the energy consumption and cost constraints of the FPGA-SoC that have limited processing, memory, and communication capacity. Despite this, the AI algorithm implementation on embedded systems can drastically reduce energy consumption and processing times, while reducing the costs and risks associated with data transmission. Therefore, its efficiency and reliability always depend on the designed prototypes. Within this range, this work proposes two different designs for the Traffic Sign Recognition (TSR) application based on the convolutional neural network (CNN) model, followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s, there is room for improvement using programmable logic (PL) and processing system (PS) in a hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers will be accelerated to provide a hardware accelerator for each layer where direct memory access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased hardware cost, and execution time optimization of 2.13 s, but, there was still room for design optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated to be a single computation engine where BRAM interface is used. The implementation results prove that our proposed embedded TSR design achieves the best performances compared to the first proposed architectures, in terms of execution time of about 0.03 s, computation roof of about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1
مقاله انگلیسی
6 AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022
Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current net- works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al- gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven meth- odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control.
keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication
مقاله انگلیسی
7 Barriers to computer vision applications in pig production facilities
موانع برنامه های بینایی کامپیوتری در تاسیسات تولید خوک-2022
Surveillance and analysis of behavior can be used to detect and characterize health disruption and welfare status in animals. The accurate identification of changes in behavior is a time-consuming task for caretakers in large, commercial pig production systems and requires strong observational skills and a working knowledge of animal husbandry and livestock systems operations. In recent years, many studies have explored the use of various technologies and sensors to assist animal caretakers in monitoring animal activity and behavior. Of these technologies, computer vision offers the most consistent promise as an effective aid in animal care, and yet, a systematic review of the state of application of this technology indicates that there are many significant barriers to its widespread adoption and successful utilization in commercial production system settings. One of the most important of these barriers is the recognition of the sources of errors from objective behavior labeling that are not measurable by current algorithm performance evaluations. Additionally, there is a significant disconnect between the remarkable advances in computer vision research interests and the integration of advances and practical needs being instituted by scientific experts working in commercial animal production partnerships. This lack of synergy between experts in the computer vision and animal health and production sectors means that existing and emerging datasets tend to have a very particular focus that cannot be easily pivoted or extended for use in other contexts, resulting in a generality versus particularity conundrum. This goal of this paper is to help catalogue and consider the major obstacles and impediments to the effective use of computer vision associated technologies in the swine industry by offering a systematic analysis of computer vision applications specific to commercial pig management by reviewing and summarizing the following: (i) the purpose and associated challenges of computer vision applications in pig behavior analysis; (ii) the use of computer vision algorithms and datasets for pig husbandry and management tasks; (iii) the process of dataset construction for computer vision algorithm development. In this appraisal, we outline common difficulties and challenges associated with each of these themes and suggest possible solutions. Finally, we highlight the opportunities for future research in computer vision applications that can build upon existing knowledge of pig management by extending our capability to interpret pig behaviors and thereby overcome the current barriers to applying computer vision technologies to pig production systems. In conclusion, we believe productive collaboration between animal-based scientists and computer-based scientists may accelerate animal behavior studies and lead the computer vision technologies to commercial applications in pig production facilities.
keywords: بینایی کامپیوتر | دامپروری دقیق | رفتار - اخلاق | یادگیری عمیق | مجموعه داده | گراز | Computer vision | Precision livestock farming | Behavior | Deep learning | Dataset | Swine
مقاله انگلیسی
8 Semantic Riverscapes: Perception and evaluation of linear landscapes from oblique imagery using computer vision
مناظر معنایی رودخانه: درک و ارزیابی مناظر خطی از تصاویر مایل با استفاده از بینایی کامپیوتری-2022
Traditional approaches for visual perception and evaluation of river landscapes adopt on-site surveys or as- sessments through photographs. The former is expensive, hindering large-scale analyses, and it is conducted only on street-level or top-down imagery. The latter only reflects the subjective perception and also entails a laborious process. Addressing these challenges, this study proposes an alternative: a novel workflow for visual analysis of urban river landscapes by combining unmanned aerial vehicle (UAV) oblique photography with computer vision (CV) and virtual reality (VR). The approach is demonstrated with an experiment on a section of the Grand Canal in China where UAV oblique panoramic imagery has been processed using semantic segmentation for visual evaluation with an index system we designed. Concurrent surveys, immersive and non-immersive VR, are used to evaluate these photos, with a total of 111 participants expressing their perceptions across multiple dimensions. Then, the relationship between the people’s subjective visual perception and the river landscape environment as seen by computers has been established. The results suggest that using this approach, rivers and surrounding landscapes can be analyzed automatically and efficiently, and the mean pixel accuracy (MPA) of the developed model is 90%, which advances state of the art. The results of this study can benefit urban planners in formulating riverside development policies, analyzing the perception of plans for a future scenario before an area is rede- veloped, and the method can also aid relevant parties in having a macro understanding of the overall situation of the river as a basis for follow-up research. Due to simplicity, accuracy and effectiveness, this workflow is transferable and cost-effective for large-scale investigations of riverscapes and linear heritage. We openly release Semantic Riverscapes—the dataset we collected and processed, bridging another gap in the field.
keywords: ریورساید | باز کردن داده ها | GeoAI | بررسی های هوایی | هواپیماهای بدون سرنشین | واقعیت مجازی | Riverside | Open data | GeoAI | Aerial surveys | Drones | Virtual reality
مقاله انگلیسی
9 Disintegration testing augmented by computer Vision technology
آزمایش تجزیه با فناوری Vision کامپیوتری تقویت شده است-2022
Oral solid dosage forms, specifically immediate release tablets, are prevalent in the pharmaceutical industry. Disintegration testing is often the first step of commercialization and large-scale production of these dosage forms. Current disintegration testing in the pharmaceutical industry, according to United States Pharmacopeia (USP) chapter 〈701〉, only gives information about the duration of the tablet disintegration process. This infor- mation is subjective, variable, and prone to human error due to manual or physical data collection methods via the human eye or contact disks. To lessen the data integrity risk associated with this process, efforts have been made to automate the analysis of the disintegration process using digital lens and other imaging technologies. This would provide a non-invasive method to quantitatively determine disintegration time through computer algorithms. The main challenges associated with developing such a system involve visualization of tablet pieces through cloudy and turbid liquid. The Computer Vision for Disintegration (CVD) system has been developed to be used along with traditional pharmaceutical disintegration testing devices to monitor tablet pieces and distinguish them from the surrounding liquid. The software written for CVD utilizes data captured by cameras or other lenses then uses mobile SSD and CNN, with an OpenCV and FRCNN machine learning model, to analyze and interpret the data. This technology is capable of consistently identifying tablets with ≥ 99.6% accuracy. Not only is the data produced by CVD more reliable, but it opens the possibility of a deeper understanding of disintegration rates and mechanisms in addition to duration.
keywords: از هم پاشیدگی | اشکال خوراکی جامد | تست تجزیه | یادگیری ماشین | شبکه های عصبی | Disintegration | Oral Solid Dosage Forms | Disintegration Test | Machine Learning | Neural Networks
مقاله انگلیسی
10 How to Build a Scalable Quantum Controller
چگونه یک کنترلر کوانتومی مقیاس پذیر بسازیم-2022
We discuss quantum computers from the perspective of one of their major building blocks, the hardware controller, explaining how it fits into the computer and the requirements and challenges it poses for engineers and scientists.
مقاله انگلیسی
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