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In the context of food science and engineering, the in vitro chewing effect on food bolus formation is a critical area of research that explores the mechanical and textural properties of ingested materials. This article presents a pioneering approach to assess the in vitro chewing impact on food bolus formation using the gray level co-occurrence matrix (GLCM) image analysis technique. As technological advancements lead to the development of mastication robots, the need for evaluating in vitro chewed food bolus has grown. To address this challenge, a case study is conducted. The study's objectives encompass utilizing GLCM to determine the in vitro chewing cycle phase, analyzing texture features, and investigating chewing trajectory differences for beef and plant-based burger patties. Applying GLCM as a methodology, the research quantitatively analyzes textural features of food bolus formations under controlled in vitro chewing conditions. The outcomes reveal distinct differences between beef and plant-based samples through GLCM parameters. Significantly, the study identifies a consistent trend across various scenarios, indicating an increase in energy and homogeneity and a decrease in dissimilarity with an increasing number of in vitro chewing cycles. This investigation offers valuable insights into the dynamic relationship between chewing cycles and textural features in the oral processing of beef and plant-based burger patties.

The deformability of elastomer surfaces offers the opportunity to capture multimodal physical information, which opens up possibilities for applications in intelligent interactive systems and flexible electronics. Accurately identifying the surface deformation of elastomers has been a key issue for understanding the contact state in a soft touch. However, effective monitoring of the deformed surface topography during soft‐touch processes is difficult due to the complexity of interfacial deformation. Herein, the optical detection method based on an ideal diffuse reflection model is proposed to monitor the deformation of the elastomer, enabling the acquisition of a spatially continuous deformation of the surface in a soft touch. This optical system with the parallel incident light can distinguish the elastomers’ surface deformation field by constructing the correlation between the reflected light irradiance and the surface deformation angle. This method allows for the perception of the contact area. Through the deep learning method, the recognition rate can reach more than 84.24% when there are slight differences in the contact of objects with different geometric shapes. The design offers valuable insights for contact state detection tasks and facilitates soft‐touch sensing functions through the high precision of optical signal acquisition.

Photoelectronic technology has found extensive application due to its non-invasiveness, compact structure, and low cost. However, in semi-transparent media, the detection system based on reflection structure indiscriminately receives reflection light from different depths, resulting in the masking of target signals and a decrease in signal quality. To address this issue, selecting reflected light at different depths through polarization gates is an effective way. In this study, we analyzed a polarization gate-semi-infinite medium scattering model and investigated the impact of various factors on the reflected light filtering capability of the polarization gate, through Monte Carlo simulations and polystyrene microsphere scattering experiments. We found that the polarization gate can achieve a more effective control effect on the high polarization area on the reflective surface. Furthermore, the signal-to-noise ratio of the photoplethysmography sensor with an orthogonally polarized gate was improved from 0.72 to 2.36 dB. In other words, the polarization gate offers new insights into signal optimization through a structural design, which facilitates the development of wearable, low-power, and robust physiological signal measurement systems in the future.

A novel soft robotic tongue is proposed for the purpose of presenting typical tongue movements in speaking, which is pneumatically actuated and supposed to be applied in pronunciation teaching device and humanoid robot to gain verisimilitude. The actuation and control system is mainly composed of solenoid proportional valves (ITV0010, SMC, Japan), DAQ card (PCIe6321, NI, USA) and motion tracking system (OptiTrack, NaturalPoint, USA). A theoretical model based on continuum mechanics is established building the relationship between input air pressure and output deformation. On the basis of the proposed theoretical model and Artificial Neural Network (ANN), an integrated control algorithm is preliminarily raised for different purpose of use in static and dynamic control. A series of verification experiments are carried out based on OptiTrack system in order to test motion controllability and working performance of the robotic tongue. The experimental results indicate that the proposed theoretical model is more suitable for static shape control due to obvious deformation delay compared with input air pressure. High deformation repeatability guarantees the precision of ANN based control method which is more suitable for dynamic motion control. The results of performance test show that actual movements of the robotic tongue generally meet the demand in demonstrating tongue shape when speaking.

Abstract Due to their outstanding nutritional and functional properties, the traditionally fermented dairy products (TFDP) from camel, mare, and cow gained universal praise during their long history of production. In this study, the physicochemical composition and microbial communities of Khoormog, Chigee, and Airag, the TFDP from Xilin Gol in China, were investigated and compared. The physicochemical analysis revealed a higher content of total solid content, protein, and fat in Khoormog (12.5 ± 1.6%; 4.6 ± 0.7%; 4.4 ± 1.3%) compared to Chigee (7.8 ± 1.3%; 2.1 ± 0.2%; 0.8 ± 0.2%) and Airag (8.9 ± 0.7%; 3.7 ± 0.4%; 1.4 ± 0.5%). All three types of TFDP shared 41.2% of bacterial and 25.4% of fungal OTUs, and 95.34% of bacterial and 95.52% of fungal sequence reads. The bacterial and fungal community consisted of four phyla and five genera, and three phyla and seven genera, respectively. Lastly, Lactobacillus predominated in Khoormog, Chigee, and Airag at the genus level, while the dominant fungal genera varied among the samples. In conclusion, the microbial community structures of TFDP from camel, mare, and cow were not significantly different in a definite area (Xilingol region), and Khoormog, Chigee, and Airag bred the common “core microbiota”.

A novel finite difference time domain (FDTD) method based on space transformations is developed that overcomes the inherent obstacles of the conventional FDTD algorithm in a spatially complex domain. Our method leads to an adaptive mesh for the investigated structure based on its geometrical shape without adding additional numerical problems such as late-time instability. In this method, mesh boundaries can follow arbitrary geometrical shapes precisely meaning that discretization errors are minimized. Such errors can be considerable when dealing with large material differences or boundary conditions within the simulation domain. Different meshing and transformation techniques for a variety of different scenarios are presented. We show how boundaries discontinuity can be handled using our method without resulting artificial singularities or zeros. Unlike previous works no dispersive medium has been used so the simulation speed is similar to the standard FDTD method. The usability and superiority of this method in terms of simulation run time and accuracy are shown through a couple of scattering and plasmonic problems. Also, the result of any simulations are validated using finite element method.

Given the payload limitation of unmanned aerial vehicles (UAVs), lightweight sensors such as camera, inertial measurement unit (IMU), and GPS, are ideal onboard measurement devices. By fusing multiple sensors, accurate state estimations can be achieved. Robustness against sensor faults is also possible because of redundancy. However, scale estimation of visual systems (visual odometry or visual inertial odometry, VO/VIO) suffers from sensor noise and special-case movements such as uniform linear motion. Thus, in this paper, a scale insensitive multi-sensor fusion (SIMSF) framework based on graph optimization is proposed. This framework combines the local estimation of the VO/VIO and global sensors to infer the accurate global state estimation of UAVs in real time. A similarity transformation between the local frame of the VO/VIO and the global frame is estimated by optimizing the poses of the most recent UAV states. In particular, for VO, an initial scale is estimated by aligning the VO with the IMU and GPS measurements. Moreover, a fault detection method for VO/VIO is also proposed to enhance the robustness of the fusion framework. The proposed methods are tested on a UAV platform and evaluated in several challenging environments. A comparison between our results and the results from other state-of-the-art algorithms demonstrate the superior accuracy, robustness, and real-time performance of our system. Our work is also a general fusion framework, which can be extended to other platforms as well.

Immersion, interaction, and imagination are three features of virtual reality (VR). Existing VR systems possess fairly realistic visual and auditory feedbacks, and however, are poor with haptic feedback, by means of which human can perceive the physical world via abundant haptic properties. Haptic display is an interface aiming to enable bilateral signal communications between human and computer, and thus to greatly enhance the immersion and interaction of VR systems. This paper surveys the paradigm shift of haptic display occurred in the past 30 years, which is classified into three stages, including desktop haptics, surface haptics, and wearable haptics. The driving forces, key technologies and typical applications in each stage are critically reviewed. Toward the future high-fidelity VR interaction, research challenges are highlighted concerning handheld haptic device, multimodal haptic device, and high fidelity haptic rendering. In the end, the importance of understanding human haptic perception for designing effective haptic devices is addressed. Keywords: Haptic display, Virtual reality, Desktop haptics, Surface haptics, Wearable haptics, Multimodal haptics

A wearable force-feedback glove is a promising way to enhance the immersive sensation when a user interacts with virtual objects in virtual reality scenarios. Design challenges for such a glove include allowing a large fingertip workspace, providing a desired force sensation when simulating both free- and constrained-space interactions, and ensuring a lightweight structure. In this paper, we present a force-feedback glove using a pneumatically actuated mechanism mounted on the dorsal side of the user’s hand. By means of a triple kinematic paired link with a curved sliding slot, a hybrid cam-linkage mechanism is proposed to transmit the resistance from the pneumatic piston rod to the fingertip. In order to obtain a large normal component of the feedback force on the user’s fingertip, the profile of the sliding slot was synthesized through an analysis of the force equilibrium on the triple kinematic paired link. A prototype five-fingered glove with a mass of 245 g was developed, and a wearable force-measurement system was constructed to permit the quantitative evaluation of the interaction performance in both free and constrained space. The experimental results confirm that the glove can achieve an average resistance of less than 0.1 N in free-space simulation and a maximum fingertip force of 4 N in constrained-space simulation. The experiment further confirms that this glove permits the finger to move freely to simulate typical grasping gestures. Keywords: Force-feedback glove, Lightweight, Large workspace, Pneumatic, Virtual reality

Force feedback gloves allow users to touch and manipulate virtual objects intuitively. Compared with gloves providing active feedback force, gloves with passive feedback force are promising in terms of safety and low weight, but simulating the variable stiffness of virtual objects is more challenging. Addressing this difficulty, we propose a five-fingered glove with passive force feedback employing a variable ratio lever mechanism. The stiffness of the proposed glove is tuned by changing the structural stiffness of this mechanism rather than by applying torque control at each joint of the finger. The switch between free and constrained space is realized in real time by locking/unlocking the revolute joints of the glove using a servo motor. Furthermore, a predictive control mode is proposed to reduce the response time of the control system, and the actual response time is less than the limit of the delay (45 ms) that humans can perceive between visual and haptic stimuli. Experimental results show that the linear stiffness at the fingertip ranges from 0.89 to 619.89 N/m, and the maximum backdrive force of the proposed glove is less than 0.147 N.

Mind wandering happens when one train of thought, related to a current undertaking, is interrupted by unrelated thoughts. The detection and evaluation of mind wandering can greatly help in understanding the attention control mechanism during certain focal tasks. Subjective assessments such as random thought-probe and spontaneous self-report are the ways previous research has assessed mind wandering. Here we propose a task in which participants are asked to simultaneously control respiration and fingertip pressure. They are instructed to click a force sensor at the exact moment of inhalation and exhalation of their respiration. The temporal synchronization between the respiratory signals and the fingertip force pulses offers an objective index to detect mind wandering. Twelve participants engaged in the proposed task in which self-reports of mind wandering are compared with the proposed objective index. The results show that the participants reported significantly more mind-wandering episodes during the trials with a larger temporal synchronization than they did during those trials with a smaller temporal synchronization. The findings suggest that the temporal synchronization might be used as an objective marker of mind wandering in attention training and exploration of the attention control mechanism.

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.

To improve the energy efficiency of the Mecanum wheel, this article extends the dynamic window approach by adding a new energy-related criterion for minimizing the power consumption of autonomous mobile robots. The energy consumption of the Mecanum robot is first modeled by considering major factors. Then, the model is utilized in the extended dynamic window approach–based local trajectory planner to additionally evaluate the omnidirectional velocities of the robot. Based on the new trajectory planning objective that minimizes power consumption, energy-reduction autonomous navigation is proposed via the combinational cost objectives of low power consumption and high speed. Comprehensive experiments are performed in various autonomous navigation task scenarios, to validate the energy consumption model and to show the effectiveness of the proposed technique in minimizing the power consumption and reducing the energy consumption. It is observed that the technique effectively takes advantage of the Mecanum robot’s redundant maneuverability, can cope with any type of path and is able to fulfil online obstacle avoidance.