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Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

Multimodal pedestrian detection has garnered significant attention due to its potential applications in complex scenarios. The complementarity characteristics between infrared and visible modalities can enhance detection performance. However, the design of cross-modal fusion mechanisms and the in-depth exploration of inter-modal complementarity still pose challenges. To address this, we propose TCCDNet, a novel network integrating cross-modal complementarity. Specifically, the efficient multi-scale attention C2f (EMAC) is designed for the backbone, which combines the C2f structure with an efficient multi-scale attention mechanism to achieve feature weighting and fusion, thereby enhancing the model’s feature extraction capacity. Subsequently, the cross-modal complementarity (CMC) module is proposed, which enhances feature discriminability and object localization accuracy through a synergistic mechanism combining channel attention and spatial attention. Additionally, a deep semantic fusion module (DSFM) based on a cross-attention mechanism is incorporated to achieve deep semantic feature fusion. The experimental results demonstrate that TCCDNet achieves a MR−2 of 7.87% on the KAIST dataset, representing a 3.83% reduction compared to YOLOv8. For the other two multimodal pedestrian detection datasets, TCCDNet attains mAP50 scores of 83.8% for FLIR ADAS and 97.3% for LLVIP, outperforming the baseline by 3.6% and 1.9% respectively. These results fully validate the effectiveness and advancement of the proposed method.

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

Small-scale pedestrian detection is one of the challenges in general object detection. Factors such as complex backgrounds, long distances, and low-light conditions make the image features of small-scale pedestrians less distinct, further increasing the difficulty of detection. To address these challenges, an Enhanced Feature-Fusion YOLO network (EFF-YOLO) for small-scale pedestrian detection is proposed. Specifically, this method employs a backbone based on the FasterNet block within YOLOv8n, which is designed to enhance the extraction of spatial features while reducing redundant operation. Furthermore, the gather-and-distribute (GD) mechanism is integrated into the neck of the network to realize the aggregation and distribution of global information and multi-level features. This not only strengthens the faint features of small-scale pedestrians but also effectively suppresses complex background information, thereby improving the accuracy of small-scale pedestrians. Experimental results indicate that EFF-YOLO achieves detection accuracies of 72.5%, 72.3%, and 91% on the three public datasets COCO-person, CityPersons, and LLVIP, respectively. Moreover, the proposed method reaches a detection speed of 50.7 fps for 1920 × 1080-pixel video streams on the edge device Jetson Orin NX, marking a 15.2% improvement over the baseline network. Thus, the proposed EFF-YOLO method not only boasts high detection accuracy but also demonstrates excellent real-time performance on edge devices.

As an accurate and convenient technique, the qRT-PCR is always used in the quantitative expression analysis of functional genes. Normalization of the data relies on stable reference genes. The fall armyworm Spodoptera frugiperda (J. E. Smith) is an important invasive and migratory pest that seriously threatens corn production around the world. In this paper, we selected 10 candidate reference genes (18S, AK, RPL10, RPS24, 28S, SOD, ATP, GAPDH, ACT, and a-TUB) and determined their expression levels under different conditions (different developmental stages, various tissues, mating status, hormones, diets, and temperatures). Subsequently, the stability of reference genes was evaluated by four algorithms (Delta Ct method, geNorm, NormFinder, BestKeeper). The optimal combination of reference genes for each treatment was obtained by geNorm. Finally, the comprehensive ranks were determined by the online tool RefFinder. Results showed that the most stable reference genes were SOD, RPL10, and RPS24 for developmental stages, α-TUB, RPL10, and ATP for different tissues, AK, RPL10, and 18S for mating status, 18S and AK under hormone treatment, 18S, RPL10, and SOD under diet treatment, RPL10, 18S, and RPS24 under temperature treatment. This study confirmed recent data on a few reference genes and provided an evaluation of a number of additional reference genes of S. frugiperda under various conditions.

Over the past few decades, tactile sensors have become an emerging field of research in both academia and industry. Recent advances have demonstrated application of tactile sensors in the area of biomedical engineering and opened up new opportunities for building multifunctional electronic skin (e-skin) which is capable of imitating the human sense-of-touch for medical purposes. Analyses have shown that current smart tactile sensing technology has the advantages of high performance, low-cost, time efficiency, and ease-of-fabrication. Tactile sensing systems have thus sufficiently matured for integration into several fields related to biomedical engineering. Furthermore, artificial intelligence has the potential for being applied in human-machine interfacing, for instance, in medical robotic manipulation, especially during minimally invasive robotic surgery, where tactile sensing is usually a problem. In this survey, we present a comprehensive review of the state of the art of tactile sensors. We focus on the technical details of transduction mechanisms such as piezoresistivity, capacitance, piezoelectricity, and triboelectric and highlight the role of novel and commonly used materials in tactile sensing. In addition, we discuss contributions that have been reported in the field of biomedical engineering, which includes its present and future applications in building multifunctional e-skins, human-machine interfaces, and minimally invasive surgical robots. Finally, some challenges and notable improvements that have been made in the technical aspects of tactile sensing systems are reported.

Accurate segmentation of thyroid nodules in ultrasound images is crucial for the diagnosis of thyroid cancer and preoperative planning. However, the segmentation of thyroid nodules is challenging due to their irregular shape, blurred boundary, and uneven echo texture. To address these challenges, a novel Mamba- and ResNet-based dual-branch network (MRDB) is proposed. Specifically, the visual state space block (VSSB) from Mamba and ResNet-34 are utilized to construct a dual encoder for extracting global semantics and local details, and establishing multi-dimensional feature connections. Meanwhile, an upsampling–convolution strategy is employed in the left decoder focusing on image size and detail reconstruction. A convolution–upsampling strategy is used in the right decoder to emphasize gradual feature refinement and recovery. To facilitate the interaction between local details and global context within the encoder and decoder, cross-skip connection is introduced. Additionally, a novel hybrid loss function is proposed to improve the boundary segmentation performance of thyroid nodules. Experimental results show that MRDB outperforms the state-of-the-art approaches with DSC of 90.02% and 80.6% on two public thyroid nodule datasets, TN3K and TNUI-2021, respectively. Furthermore, experiments on a third external dataset, DDTI, demonstrate that our method improves the DSC by 10.8% compared to baseline and exhibits good generalization to clinical small-scale thyroid nodule datasets. The proposed MRDB can effectively improve thyroid nodule segmentation accuracy and has great potential for clinical applications.

Intestinal symbiotic bacteria have formed an interdependent symbiotic relationship with many insect species after long-term coevolution, which plays a critical role in host growth and adaptation. Spodoptera frugiperda (J. E. Smith) is a worldwide significant migratory invasive pest. As a polyphagous pest, S. frugiperda can harm more than 350 plants and poses a severe threat to food security and agricultural production. In this study, 16S rRNA high-throughput sequencing technology was used to analyze the diversity and structure of the gut bacteria of this pest feeding on six diets (maize, wheat, rice, honeysuckle flowers, honeysuckle leaves, and Chinese yam). The results showed that the S. frugiperda fed on rice had the highest bacterial richness and diversity, whereas the larvae fed on honeysuckle flowers had the lowest abundance and diversity of gut bacterial communities. Firmicutes, Actinobacteriota, and Proteobacteria were the most dominant bacterial phyla. PICRUSt2 analysis indicated that most of the functional prediction categories were concentrated in metabolic bacteria. Our results confirmed that the gut bacterial diversity and community composition of S. frugiperda were affected significantly by host diets. This study provided a theoretical basis for clarifying the host adaptation mechanism of S. frugiperda, which also provided a new direction to improve polyphagous pest management strategies.

Buchnera aphidicola, an obligate endosymbiont of most aphid species, can influence aphids’ host adaptability through amino acid metabolism, potentially mediating biotype differentiation. However, its role in the biotype differentiation of Sitobion avenae remains unclear. To address this issue, six S. avenae biotypes were tested in this study. Buchnera abundance varied among biotypes fed on different wheat/barley varieties (i.e., Zhong 4 wumang, 186-TM12-34; Dulihuang, Zaoshu No.3, Xiyin No.2). The reduction in Buchnera abundance through antibiotic (rifampicin) treatment altered the virulence of five S. avenae biotypes. Based on transcriptome analysis, the differential expression of three genes (i.e., LeuB, TrpE, and IlvD) related to leucine, tryptophan, isoleucine, and valine metabolism was detected between different biotypes. Principal component analysis showed that leucine and tryptophan deficiencies most significantly impacted nymph development duration and aphid fecundity. Additionally, a neighbor-joining phylogenetic tree indicated the genetic differentiation of Buchnera among different biotypes. These results suggest Buchnera-mediated amino acid metabolism is correlated with biotype differentiation in S. avenae, although the precise mechanisms by which Buchnera influences this differentiation require further investigation. This study can offer a theoretical basis for the development of resistant crops, leading to the sustainable control of this aphid and reduced reliance on chemical insecticides.

Success of the da Vinci surgical robot in the last decade has motivated the development of flexible access robots to assist clinical experts during single-port interventions of core intrabody organs. Prototypes of flexible robots have been proposed to enhance surgical tasks, such as suturing, tumor resection, and radiosurgery in human abdominal areas; nonetheless, precise constraint control models are still needed for flexible pathway navigation. In this paper, the design of a flexible snake-like robot is presented, along with the constraints model that was proposed for kinematics and dynamics control, motion trajectory planning, and obstacle avoidance during motion. Simulation of the robot and implementation of the proposed control models were done in Matlab. Several points on different circular paths were used for evaluation, and the results obtained show the model had a mean kinematic error of 0.37 ± 0.36 mm with very fast kinematics and dynamics resolution times. Furthermore, the robot’s movement was geometrically and parametrically continuous for three different trajectory cases on a circular pathway. In addition, procedures for dynamic constraint and obstacle collision detection were also proposed and validated. In the latter, a collision-avoidance scheme was kept optimal by keeping a safe distance between the robot’s links and obstacles in the workspace. Analyses of the results showed the control system was optimal in determining the necessary joint angles to reach a given target point, and motion profiles with a smooth trajectory was guaranteed, while collision with obstacles were detected a priori and avoided in close to real-time. Furthermore, the complexity and computational effort of the algorithmic models were negligibly small. Thus, the model can be used to enhance the real-time control of flexible robotic systems.