با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
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 |
2 |
Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022 Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom-
mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to
the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind
the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in
plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing
with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the
samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm-
ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to
256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means
clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted
using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis
and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally,
the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM),
Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is
tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples. keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease |
مقاله انگلیسی |
3 |
Quantum-Enhanced Deep Learning-Based Lithology Interpretation From Well Logs
تفسیر لیتولوژی مبتنی بر یادگیری عمیق کوانتومی از Well Logs-2022 Lithology interpretation is important for understanding subsurface properties. Yet, the common manual well log
interpretation is usually with low efficiency and bad consistency.
Therefore, the automatic well log interpretation tools based
on machine learning and deep learning have been developed.
Although the state-of-the-art sophisticated models can show fine
interpretation performance with acceptable accuracies, “blind”
tests do not always exhibit satisfactory results because of the
complexity of lithology interpretation with respect to subsurface rock properties and the data-labeling quality. To solve
this generalization challenge, we propose to leverage the parameterized quantum circuits in the deep-learning model. The
quantum computing takes advantages of the superposition and
entanglement quantum systems, which could potentially endow
the generalization power or capability to the deep-learning
model. Using the proposed quantum-enhanced deep-learning
(QEDL) model, we have tested the model performance on
field well log data from different wells. Compared with the
classic fine convolutional neural network (CNN) model and the
long short-term memory (LSTM) model, the proposed QEDL
model achieves comparable model performance with a clearly
improved generalization power for interpreting both thin and
thick lithology layers. In addition, because of the quantum circuit
structure, the QEDL model needs much fewer model parameters
than LSTM and CNN models, i.e., the QEDL parameter number
in our study can be approximately 75% less than that of LSTM
and 89% less than that of CNN.
keywords: Deep learning | lithology interpretation | quantum computing. |
مقاله انگلیسی |
4 |
Quantum–Classical Image Processing for Scene Classification
پردازش تصویر کوانتومی کلاسیک برای طبقه بندی صحنه-2022 Deep-learning-based convolutional neural network (CNN) models are prominent in processing and analyzing
sensor signal data, such as images for classification. Data augmentation is a powerful technique used in training
such models to avoid overfitting and to improve accuracy. This letter proposes a data augmentation technique using
a quantum circuit for image data. The proposed quantum circuit is suitable to implement on real hardware provided by
the IBM Quantum Experience platform. In comparison with other classical data augmentation techniques, the proposed
technique increased the prediction accuracy of the CNN from 68.65 to 76.03%. However, CNN models for image
classification use many parameters during the training process. Quantum computers can efficiently handle large-scale
data inputs using qubits for information processing. Hence, we also propose a hybrid quantum–classical convolutional
neural network model (HQCNN) for scene classification. The proposed model uses a combination of CNN layers and
quantum layers to process images. The proposed HQCNN reduces parameters used for training due to the use of quantum
layers in the model. Our experimental results show that the proposed HQCNN can classify the scenes in the UC Merced
land-use dataset with an accuracy of 85.28% compared to the other models.
Index Terms—Sensor signal processing | hybrid model | quantum–classical computing | scene classification | sensor signal processing. |
مقاله انگلیسی |
5 |
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 |
6 |
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 |
7 |
یک رویکرد فیلترینگ درون حلقه ای پیشرونده شبکههای عصبی پیچشی یا همگشتی (CNN) برای کدگذاری بین فریم (Frame inter)
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 34 ساختارهای شبکههای عصبی پیچشی (CNN) برای فیلترینگ درون حلقهای طراحی شدهاند تا عملکرد کدگذاری ویدئو را بهبود بخشند. این مدلهای CNN معمولا از طریق یادگیری همبستگی بین فریمهای بازسازی شده و اصلی آموزش داده میشوند، که سپس برای هر فریم بازسازی شده اعمال میشوند تا کیفیت کلی ویدئو بهبود یابد. این استراتژی توسعه و آموزش مستقیم مدل برای درون کد گذاری موثر است زیرا یک مدل بهینه محلی کافی است. با این حال، هنگامی که برای رمزگذاری داخلی اعمال میشود، باعث فیلترینگ بیش از حد میشود، زیرا وابستگیهای مرجع درهمتنیده در میان فریمها در نظر گرفته نمیشوند. برای پرداختن به این موضوع، روشهای موجود معمولا به بهینهسازی نرخ تحریف یا اعواج (RDO) متوسل میشوند تا به صورت دلخواه از مدل CNN استفاده کنند، اما در رفع محدودیت استفاده از مدل CNN محلی موفق نیستند. در این مقاله، ما یک روش پیشرفته برای آموزش و ترکیب فیلترهای داخلی مبتنی بر CNN برای کار یکپارچه با کدکنندههای ویدئویی ارائه میکنیم. ابتدا، ما یک روش آموزش پیشرفته را برای به دست آوردن مدل داخلی توسعه میدهیم. با استفاده از یادگیری انتقالی، فریم های بازسازی شده با استفاده از مدل CNN به تدریج وارد آموزش خود مدل CNN میشوند تا وابستگیهای مرجع در کدگذاری داخلی را شبیهسازی کنند. سپس، یک استراتژی انتخاب مدل سطح چارچوب برای کدگذاری نرخ بیت بالا طراحی میکنیم که در آن اثر فیلترینگ بیش از حد رقیق میشود. نتایج تجربی نشان میدهند که روش پیشنهادی از روش RDO که تنها از مدل محلی استفاده میکند، بهتر عمل میکند. رویکرد پیشنهادی همچنین به عملکرد رمزگذاری قابل مقایسه دست مییابد اما پیچیدگی محاسباتی کمتری هنگام یکپارچه سازی مدل مترقی ما در طرح RDO دارد.
کلمات کلیدی: شبکههای عصبی پیچشی یا همگشتی (CNN) | فیلترینگ درون حلقهای | آموزش مدل | کدگذاری داخلی |
مقاله ترجمه شده |
8 |
Computer-vision classification of corn seed varieties using deep convolutional neural network
طبقه بندی بینایی ماشین انواع بذر ذرت با استفاده از شبکه عصبی پیچیده عمیق-2021 Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbor (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall 98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties.© 2021 Elsevier Ltd. All rights reserved. Keywords: Machine vision | Deep learning | Feature extraction | Non-handcrafted features | Texture descriptors |
مقاله انگلیسی |
9 |
Artificial intelligence quality inspection of steel bars installation by integrating mask R-CNN and stereo vision
بازرسی کیفیت هوش مصنوعی نصب میله های فولادی با ادغام ماسک R-CNN و دید استریو-2021 Contractors should conduct strict quality inspection of the steel bars used in concrete structures and need to automate the process of quality inspection. The objective of this study is to develop an Artificial Intelligence Quality Inspection Model (AI-QIM) that can execute quality inspection on steel bars at the construction site. The proposed AI-QIM is built on the Mask Region-based Convolutional Neural Network (Mask R-CNN) technique, which can perform instance segmentation of steel bars. This object detection technique is integrated with a stereo vision camera to generate information on steel bar installation. A contractor can use the proposed AI-QIM to estimate the quantity, spacing, diameter, and length of steel bars during quality inspection. A sample case study indicated that the AI-QIM yielded a maximum relative error of 3% when measuring steel bar spacing and a maximum relative error of 8% when measuring steel bar lengths within a range of 1–2 m from a stereo camera. Keywords: Steel bar | Quality inspection | Artificial intelligence | Convolutional Neural Network (CNN) | Mask R-CNN | Stereo vision | Object detection | Object mask | Instance segmentation |
مقاله انگلیسی |
10 |
Computer vision detection of foreign objects in coal processing using attention CNN
تشخیص بینایی ماشین اجسام خارجی در پردازش زغال سنگ با استفاده از CNN-2021 Foreign objects in coal seriously affect the efficiency and safety of clean coal production. Currently, the removal of foreign objects in coal preparation plant mainly depends on manual picking, which has disadvantages of high labor intensity and low efficiency. Therefore, there is an urgent need for rapid detection and removal of foreign objects. However, due to the inference of the background and surround objects, it is a challenge for the accurate detection of foreign objects. In this study, a convolutional neural network (CNN) with attention modules was designed to accurately segment foreign objects from a complex background in real-time. The proposed network consists of an encoder and a decoder, and the attention mechanism was introduced into the decoder to capture rich semantic information. The visualization results proved that the attention modules could focus on the features of the salient region and inhibit the irrelevant background, which significantly improved the accuracy of the detection The results showed that the proposed model correctly recognized 97% of the foreign objects in the 1871 sets of test images. The mean intersection over union (MIOU) of the optimal model was 91.24%, and the inference speed was greater than 15 fps/s, which satisfied the real-time requirement. Keywords: Foreign object detection | Model uncertainties | Attention mechanisms | Visualization |
مقاله انگلیسی |