The simulation of biological dendrite computations is vital for the development artificial intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called Dendrite Net or DD, just like support vector machine (SVM) multilayer perceptron (MLP). DD's main concept that algorithm can recognize this class after learning, if output's logical expression contains corresponding class's relationship among inputs (and \ not). Experiments and results: white-box ML showed...

Abstract The hierarchical and coordinated processing of visual information by the brain demonstrates its superior ability to minimize energy consumption maximize signal transmission efficiency. Therefore, it is crucial develop artificial synapses that integrate optical sensing synaptic functions. This study fully leverages excellent photoresponsivity properties PM6 : Y6 system construct a vertical photo-tunable organic memristor conducts in-depth research on resistive switching performance,...

Brain computer interface (BCI) based training had shown promising in treating patients who have suffered from stroke with upper limb (UL) paralysis. However, the real world study, most received comprehensive treatment, not only includes BCI but also routine training. The purpose of this study was to investigate topological alterations brain functional networks following treatment including subacute stage stroke. Twenty-five hospitalized moderate severe UL paralysis were assigned into 2...

With the rapid development of e-commerce (EC) and shopping online, accurate efficient forecasting sales (ECS) is very important for making strategies purchasing inventory EC enterprises. Affected by many factors, ECS volume range varies greatly has both linear nonlinear characteristics. Three forecast models ECS, autoregressive integrated moving average (ARIMA), neural network (NARNN), ARIMA-NARNN, are used to verify efficiency methods. Several time series from China’s Jingdong Corporation...

Objective. Modeling the brain as a white box is vital for investigating brain. However, physical properties of human are unclear. Therefore, BCI algorithms using EEG signals generally data-driven approach and generate black- or gray-box model. This paper presents first EEG-based algorithm (EEG-BCI Gang neurons, EEGG) decomposing into some simple components with meaning integrating recognition analysis activity. Approach. Independent interactive neurons regions can fully describe constructed...

When we touch an object, surface loads imposed on the skin are transmitted to thousands of specialized nerve endings (mechanoreceptors) embedded within skin. These mechanoreceptors transduce mechanical signals them into a neural code incident stimuli, enabling us feel object. To understand mechanisms tactile sensation, it is critical relationship between applied loads, state at mechanoreceptor locations, and transduced codes. In this paper, characterize bulk viscoelastic properties primate...

In this article, by combining appropriate refined Bohr's inequalities with some techniques concerning bounded analytic functions defined in the unit disk, we generalize and improve several Bohr type for such functions.

In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods. Among these, Quantum Approximate Optimization Algorithm (QAOA) stands out for its potential efficiently solve Max-Cut problem, quintessential example optimization. However, practical application faces challenges due current limitations on resource. Our work...

Emotion recognition (ER) utilizing electroencephalography (EEG) is significant in affective brain-computer interface research. Recent advances have underscored the supremacy of deep learning-based ER techniques over traditional statistical methods. Still, challenges persist extracting subject-specific and subject-shared features across temporal, spatial, frequency domains for transferable EEG-based ER. We propose an attention-enhanced naïve-gated broad learning system (ANGB) to tackle these...

A new training symbol is designed and a novel timing frequency synchronization scheme for orthogonal division multiplexing systems proposed. The accomplished by using the symmetric conjugate of in time domain finished utilizing good autocorrelation domain. simulation results show that gives very accurate estimates carrier offset provides wide acquisition range offset.

<p>Artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence recent years. The neuron of ANNs was designed by the stereotypical knowledge biological neurons 70 years ago. Artificial Neuron is expressed as f(wx+b) or f(WX). This design does not consider dendrites’ information processing capacity. However, some studies show that dendrites participate pre-calculation input data. Concretely, play a role extracting...

Moving target detection in cluttered backgrounds is always considered a challenging problem for artificial visual systems, but it an innate instinct of many animal species, especially the avian. It has been reported that spatio-temporal information accumulation computation may contribute to high efficiency and sensitivity avian tectal neurons detecting moving targets. However, its functional roles are not clear. Here we established novel computational model The proposed mainly consists three...

Abstract At present, sEMG-based gesture recognition requires vast amounts of training data; otherwise it is limited to a few gestures. Objective . This paper presents novel dynamic energy model that decodes continuous hand actions by small sEMG data. Approach The activation forearm muscles can set the corresponding fingers in motion or state with movement trends. moving store kinetic energy, and trends potential energy. each finger are dynamically allocated due adaptive-coupling mechanism...

Modulation recognition of communication signal is to confirm the modulation style in condition with much noise. Wavelet transformation has a good localization characteristic time-frequency domain, while neural network characteristics self-studying, self-adaptation, and high stabilization can improve autoimmunization intelligence recognition. We adopted ideal combination wavelet paper, firstly, we used decompose signal, then abstracted through coefficient, lastly RBF(Radial Basis Funtion)...

Cryogenic soft X-ray tomography (Cryo-SXT) is ideally suitable to image the 3D sub-cellular architecture and organization of cells with high resolution in near-native preservation state. fluorescence microscopy (Cryo-FM) can determine location a molecule interest that has been labeled fluorescent tag, thus revealing function cells. To understand relations between cells, correlative Cryo-SXT Cryo-FM was applied. This method required matching images different modalities, accuracy important....