The critical distinction between the emerging intelligent transparent surface (ITS) and intelligent reflection surface (IRS) is that the incident signals can penetrate the ITS instead of being reflected, which enables...
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In order to better monitor and manage the signal transmission in the communication system and base station, and carrier detection is more effective. This paper designs a carrier(Carrier frequency) detection system bas...
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One of the promising directions in the framework of the concept of creating the next-generation 5G/IMT-2020 networks is the development of broadband wireless networks based on autonomous unmanned aerial vehicles (UAVs...
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Cortical spreading depression (CSD) waves are widely recognized as the pathophysiological mechanism underlying migraine aura. Modeling the macroscopic phenomenological characteristics of CSD wave propagation is challe...
Cortical spreading depression (CSD) waves are widely recognized as the pathophysiological mechanism underlying migraine aura. Modeling the macroscopic phenomenological characteristics of CSD wave propagation is challenging due to the inability to capture biophysical features, while microscopic studies based on excitatory–inhibitory (E/I) neuron pairs struggle to link effectively with wave propagation behaviors. In order to couple the electrical activity of micro neurons with the macroscopic propagation behavior of the cortex, we adopt a network perspective and constructed a dual-layer ring network model. Within this unified framework, we identify four factors influencing CSD instigation and propagation: (i) the type and number of pathological neurons, (ii) the extracellular potassium concentration, (iii) the ratio of excitatory to inhibitory connections within the cortical network, and (iv) the architecture of network connectivity incorporating both short and long-range connections. Model results indicate counterintuitively that the number of initially pathological neurons does not significantly correlate with CSD propagation duration. The extracellular potassium concentration required for CSD instigation within the network is lower than that for single neurons, suggesting that coexisting cluster discharges alongside CSD may contribute to the comorbidity of epilepsy and migraine. An excessive imbalance in the E/I ratio can induce global re-entrant and retracting phenomena of CSD, whereas a higher proportion of long-range connections within the network can effectively reduce the probability of such occurrences. These findings suggest that designing intervention strategies that comprehensively consider these influential factors can effectively decrease the instigation probability of CSD or enhance the stability of brain networks during CSD propagation.
Image Classification is one of the important processing methods in the field of image processing. Traditional Classification algorithms, such as KNN (K-NearestNeighbor), have been unable to meet the accuracy standards...
Image Classification is one of the important processing methods in the field of image processing. Traditional Classification algorithms, such as KNN (K-NearestNeighbor), have been unable to meet the accuracy standards of image Classification. And convolutional neural networks (CNN) are increasingly used to solve the problem of image classification. This paper briefly ces the convolutional neural network, which is constructed using the TensorFlow deep learning framework, and conducts experiments on 2000 pieces of custom data set of 10 classification obtained by crawler technology. At the same time, a comparative experiment is conducted on the traditional image classification KNN algorithm. Experimental results show that the application of convintroduolutional neural network in image classification has relatively large advantages, and the accuracy has been greatly improved.
With the development of artificial intelligence and deep learning, image classification technology has ushered in new opportunities and challenges. The so-called image classification problem is the problem that the us...
With the development of artificial intelligence and deep learning, image classification technology has ushered in new opportunities and challenges. The so-called image classification problem is the problem that the user passes in the image, and then the computer sends out the classification and description of the content of the image. Feature description and detection as a traditional image classification method have many drawbacks, such as low accuracy and long time-consuming problems. Among the existing deep learning frameworks, Pytorch is relatively effective. Researchers can use the Pytorch framework to construct convolutional neural network models quickly and easily, and train them on massive data sets. This paper briefly introduces the image classification algorithm, neural network and Pytorch. The classification experiment is carried out on the Fashion MNIST data set, and the accuracy of the model reaches 88.39%. The future research direction is discussed.
In today's world cleaner electricity production has become an important element for resource-efficient production because modern societies are more effecting with environmental changes. Climate changes are becomin...
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ISBN:
(数字)9781728149707
ISBN:
(纸本)9781728149714
In today's world cleaner electricity production has become an important element for resource-efficient production because modern societies are more effecting with environmental changes. Climate changes are becoming more and more threatening for human. Now it is need to explore some environmentally friendly means of production and produce energy which can reduce damages for environment. This study represents a theoretical model for green tech companies working in Pakistan. Data was collected from managers of different green tech companies and it was found that there is huge potential for technology related to green production and green energy in Pakistan as the government and private sector never pay due attention to this sector. Results of this study are indicating that there is huge market space and potential of market in green energy sector of Pakistan which will help to attract other companies to invest in this sector which will help to develop clean and green environment. This study is limited up to the companies distributing solar plates in Pakistan so in future it can be extended to other means of green energy.
This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In thi...
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Most real-life data is high-dimensional tensor format, which traditional machine learning methods based on vector and matrix cannot deal with directly and result in the curse of dimensionality problems. For the sake o...
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ISBN:
(纸本)9781665413015
Most real-life data is high-dimensional tensor format, which traditional machine learning methods based on vector and matrix cannot deal with directly and result in the curse of dimensionality problems. For the sake of addressing those issues, we develop a kernelized support tensor-ring machine (KSTRM) for high-dimensional tensorial data. Specifically, tensor-ring (TR) and kernel methods are utilized to the support vector machine (SVM). And we construct a TR decomposition based kernel function. Experiments are made on real-life tensorial datasets, which confirms the superiority of an STRM over the SVM, STuM and STTM.
The fast progress of deep learning makes Convolutional Neural Network (CNN) emerges at the historic moment, and as an important achievement, it has been extensively used in all sorts of fields. Compared with tradition...
The fast progress of deep learning makes Convolutional Neural Network (CNN) emerges at the historic moment, and as an important achievement, it has been extensively used in all sorts of fields. Compared with traditional machine learning, CNN has more advantages, on the one hand, it has more hidden layers and complex network structure, and on the other hand, it has a stronger ability of feature learning and feature expression. With the fast progress of computer technology, the application research of fast and accurate recognition and classification of flowers by obtaining flower images through mobile devices has received extensive attention. The flowers images collected under natural conditions have large background interference, and it is difficult to recognize flowers because of their inter-class similarity and intra-class diversity. Therefore, in view of the lack of flower image data and low classification accuracy, t he experi m ent sorted out the data sets of four kinds of flowers, and used the CNN to classify the images. Compared with the traditional approaches, the classification precision can be largely enhanced.
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