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نتیجه جستجو - Acquisition

تعداد مقالات یافته شده: 272
ردیف عنوان نوع
1 A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination
یک سیستم بینایی کامپیوتری برای تشخیص زودهنگام آنتراکنوز در انبه قندی (Mangifera indica) بر اساس نور UV-A-2022
The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.
keywords: انبه قندی | آنتراکنوز | LDA | نور UV-A | درجه بندی | پردازش تصویر | Sugar mango | Anthracnose | LDA | UV-A light | Grading | Image processing
مقاله انگلیسی
2 Deep learning based computer vision approaches for smart agricultural applications
رویکردهای بینایی کامپیوتری مبتنی بر یادگیری عمیق برای کاربردهای کشاورزی هوشمند-2022
The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies. At the core of artificial intelligence, deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality. Computer vision techniques, in conjunction with high-quality image acquisition using remote cameras, enable non-contact and efficient technology-driven solutions in agriculture. This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting. Recent works in the area of computer vision were analyzed in this paper and categorized into (a) seed quality analysis, (b) soil analysis, (c) irrigation water management, (d) plant health analysis, (e) weed management (f) livestock management and (g) yield estimation. The paper also discusses recent trends in computer vision such as generative adversarial networks (GAN), vision transformers (ViT) and other popular deep learning architectures. Additionally, this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time. The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy. However, the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.
keywords: Agriculture automation | Computer vision | Deep learning | Machine learning | Smart agriculture | Vision transformers
مقاله انگلیسی
3 Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems
ارزیابی بیومتریک حیوانات با استفاده از بینایی کامپیوتری غیرتهاجمی و یادگیری ماشینی پیش‌بینی‌کننده خوبی برای سن و رفاه گاوهای شیری هستند: آینده سیستم‌های پشتیبانی خودکار دامپزشکی-2022
Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal welfare, contributing to the future of automated veterinary support systems. This study proposed using non- invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii) weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy cow’s face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96), slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance without signs of under or overfitting. Models developed in this study can be used in parallel with other models to assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms.
keywords: هوش مصنوعی | فیزیولوژی گاو | ماستیت | بیومتریک حیوانات | سنجش از راه دور برد کوتاه | Artificial intelligence | Cows physiology | Mastitis | Animal biometrics | Short range remote sensing
مقاله انگلیسی
4 An investigation of the transmission success in Lorawan enabled IoT-HAPS communication
An investigation of the transmission success in Lorawan enabled IoT-HAPS communication-2022
As the communication and aviation technology expand, High altitude platform stations (HAPS) are increasingly gaining a wider usage area in modern Internet of Things (IoT) deployments. One of the areas in which HAPS can be effectively utilized is the wide area deployment of sensors that require a costly data acquisition effort in terms of transportation and communication access. Aerial communication using a low-energy technology such as LoRa can provide significant advantages in such scenarios. Our work models and simulates LoRAWAN communication in utilizing HAPS in data acquisition over a large distribution span of IoT devices/sensors. We conduct experiments on various different scenarios including changing number of devices, span area, HAPS speed and LoRa duty cycle to draw conclusions about how each of these parameters affect communication quality. Results of the simulation are used in regression analysis of equation factors to calculate the expected transmission performance under different experimental setups. Our results (and simulation code) can be used to reason about certain properties of IoT deployment (such as sensor count, sensor distribution area, HAPS speed, etc.) before the real deployment is done in LoRaWAN enabled IoT-HAPS communication.
keywords: High altitude platform station communication | LoRaWAN communication | Wide-area sensor network | IoT deployment simulation | Communication quality estimation
مقاله انگلیسی
5 An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches
یک معماری تعاملی مبتنی بر اینترنت اشیا برای نظارت بی‌سیم بیماران با پچ های حسگر-2022
The alliance between the Internet of Things (IoT) and healthcare has the potential to improve healthcare assistance at different stages of care through distributed vital sign sensing, paving the way for domiciliary hospitalization. In this work, we propose an innovative design for an IoT-based interoperable healthcare system to wirelessly monitor and classify patient status. To support our research, we identify gaps, and discuss standards, protocols and technologies based on works that use relevant IoT applications in healthcare. The proposed architecture is centered on several low-energy unobtrusive sensors attached to the patients’ bodies, as well as their beds, which encompass data acquisition nodes linked to a smart gateway that aggregates data. The smart gateway is integrated with an existing hospital information system through the exchange of Electronic Health Records (EHR), making relevant patient data easily available to health professionals on systems which are familiar to them. A use case scenario is presented in order to fulfill functional and non-functional requirements and provide a better understanding of connection and communication between the distinct entities of the proposed architecture, which is based on Bluetooth Low Energy (BLE) technology at the data acquisition level, the Message Queuing Telemetry Transport (MQTT) protocol at the internal level, and on the Fast Healthcare Interoperability Resources (FHIR) standard at the higher level.
keywords: Internet of Things | Digital healthcare | Wireless patient biomonitoring | System architecture | Interoperability
مقاله انگلیسی
6 Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG-2021
Increasingly smart techniques for counterfeiting face and fingerprint traits have increased the potential threats to information security systems, creating a substantial demand for improved security and better privacy and identity protection. The internet of Things (IoT)-driven fingertip electrocardiogram (ECG) acquisition provides broad application prospects for ECG-based identity systems. This study focused on three major impediments to fingertip ECG: the impact of variations in acquisition status, the high computational complexity of traditional convolutional neural network (CNN) models and the feasibility of model migration, and a lack of sufficient fingertip samples. Our main contribution is a novel fingertip ECG identification system that integrates transfer learning and a deep CNN. The proposed system does not require manual feature extraction or suffer from complex model calculations, which improves its speed, and it is effective even when only a small set of training data exists. Using 1200 ECG recordings from 600 individuals, we consider 5 simulated yet potentially practical scenarios. When analyzing the overall training accuracy of the model, its mean accuracy for the 540 chest- collected ECG from PhysioNet exceeded 97.60 %, and for 60 subjects from the CYBHi fingertip-collected ECG, its mean accuracy reached 98.77 %. When simulating a real-world human recognition system on 5 public datasets, the validation accuracy of the proposed model can nearly reach 100 % recognition, outperforming the original GoogLeNet network by a maximum of 3.33 %. To some degree, the developed architecture provides a reference for practical applications of fingertip-collected ECG-based biometric systems and for information network security.
Keywords: Off-the-person | Fingertip ECG biometric | Human identification | Convolutional neural network (CNN) | Transfer learning
مقاله انگلیسی
7 Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes
شناسایی فرد با استفاده از پتانسیل نام-آشنا شنوایی الکترودهای EEG جلو برانگیخته-2021
Electroencephalograph (EEG) based biometric identification has recently gained increased attention of re- searchers. However, state-of-the-art EEG-based biometric identification techniques use large number of EEG electrodes, which poses user inconvenience and consumes longer preparation time for practical applications. This work proposes a novel EEG-based biometric identification technique using auditory evoked potentials (AEPs) acquired from two EEG electrodes. The proposed method employs single-trial familiar-name AEPs extracted from the frontal electrodes Fp1 and F7, which facilitates faster and user-convenient data acquisition. The EEG signals recorded from twenty healthy individuals during four experiment trials are used in this study. Different com- binations of well-known neural network architectures are used for feature extraction and classification. The cascaded combinations of 1D-convolutional neural networks (1D-CNN) with long short-term memory (LSTM) and with gated recurrent unit (GRU) networks gave the person identification accuracies above 99 %. 1D-convolutional, LSTM network achieves the highest person identification accuracy of 99.53 % and a half total error rate (HTER) of 0.24 % using AEP signals from the two frontal electrodes. With the AEP signals from the single electrode Fp1, the same network achieves a person identification accuracy of 96.93 %. The use of familiar-name AEPs from frontal EEG electrodes that facilitates user convenient data acquisition with shorter preparation time is the novelty of this work.
Keywords: Auditory evoked potential | Biometrics | Deep learning | Electroencephalogram | Familiar-name | Person identification
مقاله انگلیسی
8 The application of reusable learning objects (RLOs) in preparation for a simulation laboratory in medication management: An evaluative study
استفاده از اشیاء یادگیری قابل استفاده مجدد (RLOS) در آماده سازی یک آزمایشگاه شبیه سازی در مدیریت دارو: یک مطالعه ارزیابی-2021
To enhance the preparedness of undergraduate nursing and midwifery students to participate in the safe provision of medication administration on their clinical placements, an innovative blended learning strategy was designed and developed by the authors. The blended learning strategy included a suite of online reusable learning objects specific to medication management theoretical knowledge and psychomotor skills to prepare students for a 90-minute practical face to face simulation laboratory session. Students identified that the reusable learning objects had prepared them for the simulation laboratory session and was rated as a productive learning experience. The blended learning strategy implemented to teaching and learning medication management to undergraduate nursing and midwifery students can positively influence students’ acquisition of knowledge and psychomotor skills to safely administer medications prior to their practice placements in a clinical setting.
keywords: یادگیری تلفیقی | مدیریت دارو | اشیاء یادگیری قابل استفاده مجدد | شبیه سازی | Blended learning | Medication management | Reusable learning objects | Simulation
مقاله انگلیسی
9 ECB2: A novel encryption scheme using face biometrics for signing blockchain transactions
ECB2: یک طرح رمزگذاری جدید با استفاده از بیومتریک چهره برای امضای تراکنش های بلاک چین-2021
Blockchain is the technology on the basis of the recent smart and digital contracts. It ensures at this system the required characteristics to be effectively applied. In this work, we propose a novel encryption scheme specifically built to authorize and sign transactions in digital or smart contracts. The face is used as a biometric key, encoded through the Convolutional Neural Network (CNN), FaceNet. Then, this encoding is fused with an RSA key by using the Hybrid Information Fusion algorithm (BNIF). The results show a combined key that ensures the identity of the user that is executing the transaction by preserving privacy. Experiments reveal that, even in strong heterogeneous acquisition conditions for the biometric trait, the identity of the user is ensured and the contract is properly signed in less than 1.86 s. The proposed ECB2 encryption scheme is also very fast in the user template creation (0.05s) and requires at most four attempts to recognize the user with an accuracy of 94%.
مقاله انگلیسی
10 Construction of carbonate reservoir knowledge base and its application in fracture-cavity reservoir geological modeling
ساخت پایگاه دانش مخزن کربناته و کاربرد آن در مدلسازی زمین شناسی مخزن شکستگی-حفره ای-2021
To improve the efficiency and accuracy of carbonate reservoir research, a unified reservoir knowledge base linking geological knowledge management with reservoir research is proposed. The reservoir knowledge base serves high-quality analysis, evaluation, description and geological modeling of reservoirs. The knowledge framework is divided into three categories: technical service standard, technical research method and professional knowledge and cases related to geological objects. In order to build a knowledge base, first of all, it is necessary to form a knowledge classification system and knowledge description standards; secondly, to sort out theoretical understandings and various technical methods for different geologic objects and work out a technical service standard package according to the technical standard; thirdly, to collect typical outcrop and reservoir cases, constantly expand the content of the knowledge base through systematic extraction, sorting and saving, and construct professional knowledge about geological objects. Through the use of encyclopedia based collaborative editing architecture, knowledge construction and sharing can be realized. Geological objects and related attribute parameters can be automatically extracted by using natural language processing (NLP) technology, and outcrop data can be collected by using modern fine measurement technology, to enhance the efficiency of knowledge acquisition, extraction and sorting. In this paper, the geological modeling of fracture-cavity reservoir in the Tarim Basin is taken as an example to illustrate the construction of knowledge base of carbonate reservoir and its application in geological modeling of fracture-cavity carbonate reservoir.
keywords: knowledge management | reservoir knowledge base | fracture-cavity reservoir | geological modeling | carbonates | paleo-underground river system | Tahe oilfield | Tarim Basin
مقاله انگلیسی
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