A bidirectional in vitro brain–computer interface(BCI)directly connects isolated brain cells with the surrounding environment,reads neural signals and inputs modulatory *** a noninvasive BCI,it has clear advantages i...
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A bidirectional in vitro brain–computer interface(BCI)directly connects isolated brain cells with the surrounding environment,reads neural signals and inputs modulatory *** a noninvasive BCI,it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural ***,the core of ex vivo BCIs,microelectrode arrays(MEAs),urgently need improvements in the strength of signal detection,precision of neural modulation and ***,nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution,as well assupporting long-term cultivation of *** is enabled by the advantageous electrochemical characteristics of nanomaterials,such as their active atomic reactivity and outstanding charge conduction efficiency,improving the performance of ***,we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary *** also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.
Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based *** consuming time and resources,intrusive traffic hampers the efficient operation of network *** effective st...
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Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based *** consuming time and resources,intrusive traffic hampers the efficient operation of network *** effective strategy for preventing,detecting,and mitigating intrusion incidents will increase productivity.A crucial element of secure network traffic is Intrusion Detection system(IDs).An IDssystem may be host-based or network-based to monitor intrusive network *** unusual internet traffic has become a severe security risk for intelligent *** systems are negatively impacted by several attacks,which are slowing *** addition,networked communication anomalies and breaches must be detected using Machine learning(ML).This paper uses the NsL-KDD data set to propose a novel IDsbased on Artificial Neural Networks(ANNs).As a result,the ML model generalizessufficiently to perform well on untried *** NsL-KDD dataset shall be utilized for both training and *** this paper,we present a custom ANN model architecture using the Keras open-source software *** specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network *** performance of the ANN is carefully tested and evaluated,resulting in the identification of a maximum detection accuracy of 97.5%.We thoroughly compared our suggested model to industry-recognized benchmark methods,such as decision classifier combinations and ML classifiers like k-Nearest Neighbors(KNN),Deep learning(DL),support Vector Machine(sVM),Long short-Term Memory(LsTM),Deep Neural Network(DNN),and *** is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all *** result underlines the effectiveness of the suggested methodology by demonstrating the ANN’s capacity to accurately assess the effectiveness of the developed strategy
In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,a...
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In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,and BCC+FCC phase of high-entropy alloys(HEAs).Considering the lack of data,CGANs are introduced for data augmentation,and AL can achieve high prediction accuracy under a small sample size owing to itsspecial sample selection ***,we propose an ML framework combining CGAN and AL to predict the phase of *** arithmetic optimization algorithm(AOA)is introduced to improve the artificial neural network(ANN).AOA can overcome the problem of falling into the locally optimal solution for the ANN and reduce the number of training *** AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test *** improve the performance and interpretability of the model,domain knowledge is incorporated into the feature ***,considering that the proposed method can alleviate the problem caused by the shortage of experimental data,it can be applied to predictionsbased on small datasets in other fields.
A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple *** inverse kinematic solution i...
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A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple *** inverse kinematic solution is obtained through an analytical method,and the trajectory is planned in joint *** such,the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical,obstacle and task constraints,and the optimization variables are significantly *** teaching-learning-based optimization(TLBO)algorithm is employed to search for the redundant parameters to generate an optimal *** and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the ***,the planned trajectory has no theoretical end-effector deviation for the task *** approach can provide a reference for the motion planning of other redundant manipulators.
For the past three years, the project-basedlearning (PBL) approach was used to teach electromagnetics and wave concepts in an engineering course, by the implementation of three main projects: 2014—electromagnetic cr...
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High-frequency quantitative trading in the emerging digital currency market poses unique challenges due to the lack of established methods for extracting trading information. This paper proposes a deep evolutionary re...
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On the basis of practicality, ceramic art design constantly increases its aesthetic artistry, bringing renewal and enjoyment to human life. Ceramic art is a complex art form, and the aesthetic feeling of its works is ...
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Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detec...
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Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and *** the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character ***,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different ***,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original *** maps are then subjected to a differentiable binarization process,resulting in an approximate binarization *** map helps to identify the boundaries of the text ***,the text detection box is generated after the post-processing *** the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original squeeze-and-Excitation(sE)block is replaced by the efficient shuffle Attention(sA)that integratesspatial and channel attention,improving the accuracy of Yi characters ***,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text *** experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,*** also,we have compared the detection and re
Resistive random access memory(RRAM)enables the functionality of operating massively parallel dot prod-ucts and ***-based accelerator issuch an effective approach to bridging the gap between Internet of Things device...
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Resistive random access memory(RRAM)enables the functionality of operating massively parallel dot prod-ucts and ***-based accelerator issuch an effective approach to bridging the gap between Internet of Things devices'constrained resources and deep neural networks'tremendous *** to the huge overhead of Analog to Digital(A/D)and digital accumulations,analog RRAM buffer is introduced to extend the processing in analog and in *** analog RRAM buffer offers potential solutions to A/D conversion issues,the energy consumption isstill challenging in resource-constrained environments,especially with enormous intermediate data ***,criti-cal concerns over endurance must also be resolved before the RRAM buffer could be frequently used in reality for DNN in-ference *** we propose LayCO,a layer-centric co-optimizing scheme to address the energy and endurance con-cerns altogether while strictly providing an inference accuracy *** relies on two key ideas:1)co-optimizing with reduced supply voltage and reduced bit-width of accelerator architectures to increase the DNN's error tolerance and achieve the accelerator's energy efficiency,and 2)efficiently mapping and swapping individual DNN data to a correspond-ing RRAM partition in a way that meets the endurance *** evaluation with representative DNN models demonstrates that LayCO outperforms the baseline RRAM buffer based accelerator by 27x improvement in energy effi-ciency(over TIMELY-like configuration),308x in lifetime prolongation and 6x in area reduction(over RAQ)while main-taining the DNN accuracy loss less than 1％.
This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also *** a reliable and quantitative evaluation...
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This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also *** a reliable and quantitative evaluation of AI fairness,many associated concepts have been proposed,formulated and ***,the inexplicability of machine learningsystems makes it almost impossible to include all necessary details in the modelling stage to ensure *** privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating AI fairness are *** imbalance between algorithms’utility and humanization has further reinforced *** for federated learningsystems,these constraints on precision AI fairnessstill *** solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.
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