This paper proposes a new turning theory for spherical robots, which better describes the turning mechanism of spherical robots under turning constraints, using a pendulum-driven spherical robot as an example. Compared to the previous turning theory, the new theory shows greater alignment with real-world data, especially at high speeds. Based on this new turning theory, we have constructed and optimized a new kinematic model and used this model to design a trajectory tracking algorithm that remains reliable even at high speeds. Physical experiments demonstrate that the new algorithm significantly improves trajectory tracking accuracy at high speeds. Through enhancements to the trajectory tracking algorithm, this study improves the autonomous cruising speed of spherical robots.

Objective assessment of taste sensation is essential for medical diagnosis, food development, and multisensory interaction. Human taste sensation can be characterized through biosignals such as electroencephalography (EEG) and electromyography (EMG). However, taste sensation recognition on multiple-subject datasets remains challenging due to the low signal-to-noise ratio and substantial individual variability of biosignals. To address these problems, we propose SwinTaste for accurate and generalized taste sensation recognition from bimodal biosignals. The Transformer is introduced to extract hierarchical features. A two-stage patch partition module is optimized for the characteristics of biosignals. Moreover, a multi-task learning strategy is adopted to improve the generalization and subject adaptation abilities. The SwinTaste is evaluated on a multiple-subject taste sensation dataset. Comparison experiments and ablation studies demonstrate the superior performance of SwinTaste, indicating the potential for generalized application in biosignal recognition.

Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition.

This article proposes an adaptive and robust terrain classification control algorithm for a pendulum-driven spherical robot, aiming to solve the problem of insufficient control accuracy caused by using the same controller for different terrains. The common terrains are classified into three categories, and a terrain classification dataset is established based on the vibration signal of the robot. Using LightGBM, combined with the feature window and window voter algorithm proposed in this article, the terrain classification results are corresponded with three proposed controllers. Physical experiment results show that the proposed classification control algorithm can work stably in different terrains, guiding the spherical robot to select the optimal controller to improve its motion performance.

Path planning is a crucial module in motion planning for autonomous driving, aiming at generating kinematically feasible and collision-free paths. Furthermore, the smoothness of generated path is significant for passengers’ comfortable feelings. In this paper, we propose an improved quadratic programming approach that generates optimal paths in urban structure scenarios with the Frenét frame, taking the cost of the path curvature into consideration explicitly. The proposed second-order Taylor-expansion estimation of the path curvature with the lateral spatial parameters can reflect the actual change of path curvature. Various simulated scenarios verify the effectiveness of our proposed method and the improvement of path quality by adding the curvature objective in the optimization procedure. The source code is released as an open-source package for the community.

In this paper, a fuzzy PID controller based on yaw angle prediction is applied to design an attitude controller for a spherical rolling robot. The robot consists of a 2-DOF pendulum located inside a spherical shell with freedom to rotate about the transversal and longitudinal axis. The proposed controller allows the robot to autonomously change its parameters to adapt to different environments based on current state. The past researches on the motion of spherical robots mostly focused on simulation or ideal experimental environment. But in this paper, a physical system is built and experiments are carried out to demonstrate the effectiveness, robustness and adaptability of the controller.

BACKGROUND: Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. RESULTS: ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. CONCLUSIONS: As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

Surface electromyography (sEMG) has been proven competent and reliable to recognize speech musculature movement patterns. In other words, we can understand what a person prepares to say by collecting sEMG signals around the mouth. Therefore, sEMG-based Mime Speech Recognition (MSR) is a potential technique for human-machine interaction within noisy surroundings as well as the application of helping dysarthric patients. In this paper, we introduce multi-layer Bidirectional Long Short-Term Memory (BLSTM) networks with attention mechanism as a classifier for MSR, and verify it in the data set collected by ourselves. Six-channel sEMG signals are firstly acquired from elaborately selected facial muscles. Short-time Fourier Transform (STFT) and Convolutional Neural Networks (CNN) are utilized to extract time-frequency domain feature maps, replacing the handcrafted features in classic methods. The second phase of recognition process lies in the designed classifier. This classification system achieves over 97% accuracy in the four-class MSR task, significantly surpassing simple CNN and LSTM methods. Such result also indicates that excellent MSR results can be achieved without relying on handcrafted signal features.

A new method to generate color texture images is proposed in this paper, which derived from our previous works to generate the gray texture. The method is based on the olfactory system bionic model to generate the gray texture. The model architecture mimics that of mammal olfactory neural system. Period function is used as the activity function of nodes in the model to realize the periodic repetition of texture. Chaotic mapping is used to adjust the model parameters to assure the model being in non-convergence state. The previous input is introduced as the noise to simulate the background noise of neural system. One color image is used as seed image. In HSV space, the Hue (H), Saturation (S) and Value (V) of each pixel is used as the model input and the model output is composed as the H, S and V of corresponding pixel in generated texture. Experimental results show that the proposed method can generate many beautiful color textures, whose textures are different from the original texture.

Though the olfactory model entitled KIII has been widely used to pattern recognition, it only can give bare prediction. Combining EM model with the transductive confidence machine, a novel method to recognize hypoxia electroencephalogram (EEG) with a preset confidence level is proposed in this paper. This method can make prediction with confidence measure rather than bare prediction. The experimental results of classifying normal and hypoxia EEGs show that the method can set confidence level in advance for every prediction to control the risk of error effectively.

Modeling olfactory neural systems, the Kill model proposed by Freeman exhibits chaotic dynamic characteristics and has potential for pattern recognition. Fuzzy c-means clustering can classify an object to several classes at the same time but with different degrees based on fuzzy sets theory. Based on the Kill model, mandarin digital speech is recognized utilizing the features extracted by the fuzzy c-means clustering. Experimental results show that the Kill model can perform digital speech recognition efficiently and the fuzzy c-means clustering has better performance than the hard k-means clustering.

This paper reports a novel method to classify EEGs from subjects under normal and hypoxia conditions, which provides a potential efficient indicator to evaluate hypoxia in real time. The EEG data are collected from 3 healthy subjects while their neurobehaviors are evaluated to assess the degree of hypoxia. Together with Approximate entropy (ApEn), the specific energy in a sub-band of 30-60 Hz of the Welch power-spectral-density (PSD) is extracted as the features. Bayesian classifier, 3-layer perceptron established by back-propagation and SVM are utilized for classification, respectively. The accuracy of Bayesian classifier is over 90.8% on test set. We compared the performance in terms of changing the architecture of the net. The accuracy of BP network reaches 94.2% on test set. Meanwhile, a SVM with Polynomial kernel revealed an accuracy over 92.5% on the test set. The experimental results show that the hypoxia EEG can be distinguished from normal one for individuals remarkably.

To model mammalian olfactory neural systems, a chaotic neural network entitled K-set has been constructed. This neural network with non-convergent "chaotic" dynamics simulates biological pattern recognition. This paper reports the characteristics of the KIII set and applies it to digital classification of the sounds of Mandarin spoken digits. Experimental results show that the KIII set performs digital speech recognition efficiently.