Knowing the connectivity patterns in neural circuitry is essential to understand the operating mechanism of the brain, as it allows the analysis of how neural signals are processed and flown through the neural system....
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Knowing the connectivity patterns in neural circuitry is essential to understand the operating mechanism of the brain, as it allows the analysis of how neural signals are processed and flown through the neural system. With the recent advances in neural recording technologies in terms of channel size and time resolution, a simple and efficient system to perform neural connectivity inference is highly desired, which will enable the process of high dimensional neural activity recording data and reduction of the computational time and cost. In this work, we show that the spike-timing dependent plasticity(STDP) algorithm can be used to reconstruct neural connectivity patterns in a biological neural network, with higher accuracy and efficiency than statistic-based inference methods. The biologically inspired STDP learning rules are natively implemented in a second-order memristor network and are used to estimate the type and the direction of neural connections. When stimulated by the recorded neural spike trains, the memristor device conductance is modulated by the proposed STDP learning rules, which in turn reflects the correlation of the spikes and the possibility of neural connections. By compensating for the different levels of neural activity, highly reliable inference performance can be achieved. The proposed approach offers real-time and local learning, resulting in reduced computational cost/time and strong tolerance to variations of the neural system.
Magnetic field focusing in longitudinal direction has been a missing link for three-dimensional synthesized magnetic focusing (3-D SMF). Deep magnetic focusing (DMF) by multiple coaxial coils, a sort of 3-D SMF, is fi...
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Federated learning (FL) offers a decentralized training approach for machine learning models, prioritizing data privacy. However, the inherent heterogeneity in FL networks, arising from variations in data distribution...
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"Integration of blockchain and fog computing" involves the two different concepts, i.e. blockchain technology and fog computing which has something in common—the decentralised system. The rapid advancement ...
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Heart failure is now widely spread throughout the *** disease affects approximately 48%of the *** is too expensive and also difficult to cure the *** research paper represents machine learning models to predict heart ...
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Heart failure is now widely spread throughout the *** disease affects approximately 48%of the *** is too expensive and also difficult to cure the *** research paper represents machine learning models to predict heart *** fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for *** supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best *** boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data *** and Scikit-learns are used for *** Flow and Keras,along with Python,are used for ANN model *** DT and RF algorithms achieved the highest accuracy of 95%among the ***,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.
This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration ...
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This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.
Technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching metho...
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This paper presents a lossless adaptive-resolution compression technique intended for energy-constrained implantable neural interface microsystems. The proposed method seamlessly integrates with the widely-adopted ADC...
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The next generation of immersive applications, such as eXtended reality (XR), will likely be cloud-based and streamed over mobile networks using myriad technologies such as WiFi and 6th-generation mobile networks. Mob...
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The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large langua...
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