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نتیجه جستجو - ICME

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Long-range connections enrich cortical computations
اتصالات دوربرد غنی سازی محاسبات قشر-2020
The cerebral cortex can perform powerful computations, including those involved in higher cognitivefunctions. Cortical processing for such computations is executed by local circuits and is further enrichedby long-range connectivity. This connectivity is activated under specific conditions and modulates localprocessing, providing flexibility in the computational performance of the cortex. For instance, long-rangeconnectivity in the primary visual cortex exerts facilitatory impacts when the cortex is silent but sup-pressive impacts when the cortex is strongly sensory-stimulated. These dual impacts can be captured bya divisive gain control model. Recent methodological advances such as optogenetics, anatomical tracing,and two-photon microscopy have enabled neuroscientists to probe the circuit and synaptic bases of long-range connectivity in detail. Here, I review a series of evidence indicating essential roles of long-rangeconnectivity in visual and hierarchical processing involving numerous cortical areas. I also describe anoverview of the challenges encountered in investigating underlying synaptic mechanisms and highlightrecent technical approaches that may overcome these difficulties and provide new insights into synapticmechanisms for cortical processing involving long-range connectivity
Keywords:Long-range connectivity | Hierarchical processing | Visual cortex | Gain control | Sensory-Motor interaction | Synaptic mechanism
مقاله انگلیسی
2 Investigation of the optimal method for generating and verifying the Smartphone’s fingerprint: A review
بررسی روش بهینه برای تولید و تأیید اثر انگشت گوشی هوشمند: یک بررسی-2020
The technical transformation and transfer of most services to digital platforms require that everyone has an electronic device connected to the Internet to assist them accomplish their tasks. Smartphones are one of the best options for everyone because of their small size and ease of transport, in addition to their high capabilities equivalent to a personal computer. It is necessary to identify these devices, usually by checking their IMEI (International Mobile Equipment Identity), to provide and manage several services like the cellular network service. On the other hand, criminals and counterfeiters can manipulate this identity to hide the device and prevent it from being tracked or to make high profits from selling substandard devices. Therefore, several recent proposals have emerged to create a strong fingerprint for use in device identification purposes. This paper reviews and discusses the existing methods to generate a device identity and defines their gaps. Also, it classifies the methods into four categories based on the used technique, namely PUF, machine learning, comparison approach, and sensor calibration. Additionally, it introduces the factors to consider when choosing the technique of device identification. It provides a list of possible attacks on each technology used in device identification methods.
Keywords: Device identification | IMEI cloning | ICMetric | Fingerprinting | Identity verification | PUF
مقاله انگلیسی
3 Deep learning based predictive modeling for structure-property linkages
مدل سازی پیش بینی مبتنی بر یادگیری عمیق برای پیوندهای ساختار و ویژگی-2019
Crystal plasticity finite element method (CPFEM) based simulations have been traditionally used for analyses of deformation in metals. However, CPFEM simulations are computationally expensive, especially for problems like fatigue where analyses are based on deformation cycles. Moreover, correlations of structure-property linkages based on homogenization and localization are not easily conceived. In this work deep learning based models have been proposed that are able to predict macroscopic properties based on features extracted from the microstructure with minimal human bias. The model is able to predict property against a given structure within dual phase, isotropic elastic-plastic regime. A systematic approach for finding optimal depth and width of neural network has been identified that reduces the overall development effort. It is observed that in the absence of a large training dataset, performance of a convolutional neural network (CNN) model degrades if too many layers and/or too many neurons are used. The CNN model is able to identify soft and hard regions of microstructures and is able to correlate structure-property relation in forward sense i.e. for homogenization. In this work, it has been demonstrated that human intervention is not needed for feature extraction and selection leading to minimization of researcher’s bias. The drawback of CNN model interpretability is overcome by using Respond-CAM feature visualization.
Keywords: Machine learning | Crystal plasticity | Convolutional neural networks | Micromechanics | Deep learning | ICME
مقاله انگلیسی
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