Abstract:

Abstract:

Flexible endoscopic robots, with their continuum structural characteristics, demonstrate unique advantages in minimally invasive surgery. However, the inherent nonlinear deformation features of continuum structures pose significant challenges to motion control precision. To address this technical bottleneck, this paper proposes an optimal teleoperation control method for flexible endoscopic robots based on neurodynamic optimization. First, a master-slave motion mapping mechanism in image space is established, coupled with a kinematic model of the flexible endoscope, to achieve accurate mapping between image feature velocities and driving velocities. Second, joint motion constraints are incorporated to formulate the robot control as a quadratic programming based optimal control problem, which is efficiently solved using a neurodynamic-based real-time solver. Experimental validation is conducted on a ureteroscopic robotic platform. Results demonstrate that the proposed method effectively suppresses manual operation errors and velocity oscillations, maintaining target tracking errors within 2.5% while significantly enhancing the accuracy and stability of instrument manipulation during lithotripsy procedures.

Abstract:

Cervical cancer is one of the leading causes of cancer-related death among women globally, and radiotherapy is a common treatment method for cervical cancer. Among the various radiation therapies, brachytherapy, which involves placing the radiation source directly into an area close to the tumor, can deliver a high dose of radiation directly to the tumor, making it more applicable compared to other radiation methods. Accurate segmentation of organs-at-risk is crucial for accurately estimating radiotherapy doses and maximizing protection of normal tissues from radiation damage. However, automatic segmentation of tubular structures, such as the colon and rectum, remains challenging. Factors such as intestinal folds and motion artifacts can affect the segmentation performance, and the presence of the radiation source in brachytherapy can degrade CT image quality, further impacting the segmentation results. This paper proposes a method for the segmentation of tubular organ-at-risk in cervical cancer based on centerline and distance map information. By enhancing the network’s understanding of anatomical structures, the method improves the identification of the topological structure of tubular organs and their spatial relationships within the human body, thus improving segmentation accuracy and optimizing radiotherapy dose distribution. Through experimental evaluation on a cervical cancer brachytherapy dataset, performance analysis was conducted using metrics such as Dice similarity coefficient (DSC), intersection over union (IoU), Recall, and 95% Hausdorff distance (HD95). The experimental results show that the proposed method outperforms the baseline network ResUNet in most metrics, specifically with a DSC of 71.58%, an IoU of 52.12%, a Recall of 79.03%, which improve by 11.29%, 7.84% and 12.70%, respectively, compared to ResUNet. The HD95 is 10.06, which is a decrease of 1.76 compared to ResUNet. The results indicate that the proposed method effectively improves the segmentation accuracy of the colon and rectum in cervical cancer brachytherapy CT images, reducing the impact of complex organs and image quality on the segmentation results.

Abstract:

Multiple-instance learning (MIL), as a weakly supervised learning method, has been widely applied in the field of medical image analysis in recent years. The paper reviews the progress of MIL applications in whole slide images, with a detailed analysis of its roles in tumor detection, subtype classification, and survival prediction. MIL holds unique advantages in weakly supervised learning , which can be optimized and extended through the introduction of new mechanisms to adapt to a broader range of application scenarios. The paper first reviews some widely used or uniquely advantageous MIL models, elaborating on their technical features and specific application contexts. Secondly, it introduces the application and technology advancements of MIL in multimodal medical image analysis. Finally, the current research progress of MIL is summarized, and its future development prospects are explored.

Abstract:

Non-contact vital sign monitoring using millimeter-wave radar offers continuous and discreet identification. Cardiac motion is influenced by various complex factors, making it challenging to capture characteristic waveform information. To address this, the study employs millimeter-wave radar transmitting frequency modulated continuous waves to monitor and record cardiac data during sleep. Additionally, the paper proposes a deep convolutional neural network (CNN)-based identity recognition method using one-dimensional time-series radar signals of cardiac motion. The performance of this method is compared with three deep learning algorithms: long short-term memory Network, InceptionTime, and LSTformer. The final classification accuracies of all models exceed 85% on a dataset of heart signals collected in a resting state in the laboratory. Among the models, InceptionTime achieves the highest accuracy but requires the longest processing time. The long short-term memory and LSTformer models exhibit lower accuracy but faster processing. The CNN model proposed in this study demonstrates comparable accuracy to InceptionTime, while requiring less computational time, thus balancing accuracy and efficiency.

Abstract:

Image denoising methods based on deep learning have effectively solved the problems of cumbersome parameter tuning and complex noise modeling in traditional denoising methods. However, the model training of supervised learning relies heavily on pairs of clean and noisy images, which limits the wide application of such models. Unsupervised learning denoising models only require single noisy images for training, but the existing unsupervised denoising methods still have the problem that it is difficult to balance network training efficiency and denoising performance. This paper proposes an efficient image denoising method, which improves the efficiency of denoising model training. Specifically, this method proposes a deep neighbor downsampler, which is used to obtain similar image pairs for training the noise model from the same noisy image. The research proposed sampler method not only meets the requirements that the pixels of the image pairs are adjacent and the appearances are similar, but also the deep neighbor downsampling discards some redundant information and avoids heavy dependence on assumptions about the noise distribution. Finally, the research verify the effectiveness of the research method through synthetic experiments with various noise distributions in the standard red green blue space and real image experiments. The experimental results confirm that the sampling strategy the research proposed effectively overcomes the balance problem between training efficiency and denoising performance.

Abstract:

As a vital part of traditional Chinese medicine, acupuncture has extensive application value all over the world. However, the reliance on practitioners’ experience for acupoint localization in traditional acupuncture methods leads to a lack of standardization, restricting its reproducibility and broader adoption. Acupuncture robots, as a kind of intelligent medical devices, offer new opportunities for standardizing and promoting acupuncture techniques. This paper introduces an improved YOLOv8-Pose model, YOLO-PointMap, designed to address challenges in dense acupoint distribution and weak feature recognition. By incorporating dynamic convolution to optimize the C2f module and introducing a channel-attention-based feature fusion module, the model achieves significant advancements in multi-scale feature extraction and integration. Experimental results show that the end point error (EPE), percentage of correct keypoints (PCK) and mAP50-95(Pose) indexes of YOLO-PointMap on the test set are superior to the existing models, with the values reaching 3.27, 1.00 and 84.90% respectively, especially in dense key point identification and weak feature region localization. It provides strong support for the development of acupuncture robot technology, and shows the potential application value in the fields of virtual reality and intelligent interaction.

Abstract:

For cone beam computed tomography (CBCT), there has long been a desire to modulate the intensity and distribution of the X-rays to accommodate the patient’s anatomy as the gantry rotates from one projection to another. This would reduce both image artifacts and radiation dose. However, the current beam modulation setups, such as dynamic bowtie filters, may be too complex for practical use in clinical applications. This study aimed to investigate a simplified dynamic beam filtration strategy for CBCT imaging to reduce image artifacts and radiation dose. In this study, the beam filtration was designed to vary dynamically as the CBCT gantry rotates around the object. Specifically, two distinct components were integrated: the sheet filter part and the bowtie filter part. The dynamic beam filtration setup has two working schemes, one is a combination of dynamic sheet filter and dynamic bowtie filter, denoted as dynamic filter-dynamic bowtie (DFDB); the other is a combination of dynamic sheet filter and static bowtie filter, denoted as dynamic filter-static bowtie (DFSB). Numerical imaging experiments were performed for three human body parts: the shoulder, chest, and knee. In addition, the Monte Carlo simulation platform MC-GPU was used to generate the dose distribution maps. Results showed that the proposed DFDB and DFSB beam filtration schemes can significantly reduce the image artifacts and thus improve the CBCT image quality. Depending on the scanned object, the total radiation dose could be reduced by 30%. The proposed simple dynamic beam filtration strategy, especially the DFSB approach, could be beneficial in the future to improve the CBCT image quality with reduced image artifacts and radiation dose.

Abstract:

The ability to replicate the microenvironment of the human body through the fabrication of scaffolds is a significant achievement in the biomedical field. However, the search for the ideal scaffold is still in its infancy and there are significant challenges to overcome. In the modern era, the scientific community is increasingly turned to natural substances due to their superior biological ability, lower cost, biodegradability, and lower toxicity than synthetic lab-made products. Chitosan is a well-known polysaccharide that has recently garnered a high amount of attention for its biological activities, especially in 3D bone tissue engineering. Chitosan closely matches the native tissues and thus stands out as a popular candidate for bioprinting. This review focuses on the potential of chitosan-based scaffolds for advancements and the drawbacks in bone treatment. Chitosan-based nanocomposites have exhibited strong mechanical strength, water-trapping ability, cellular interaction, and biodegradability. Chitosan derivatives have also encouraged and provided different routes for treatment and enhanced biological activities. 3D tailored bioprinting has opened new doors for designing and manufacturing scaffolds with biological, mechanical, and topographical properties.

Abstract:

Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, is clinically utilized for sedation, anesthesia, and the treatment of refractory depression. However, its addictive properties restrict its broader clinical application. A dose of 0.5 mg/kg is commonly used as an antidepressant in clinical settings, while 15 mg/kg represents the dose typically associated with abuse. The effects of varying doses of ketamine on brain network activation remain unclear. In this experiment, two representative doses of ketamine, 0.5 mg/kg and 15 mg/kg, were administered via intraperitoneal injection for 7 consecutive days. Brain network activation was assessed by examining the expression of the immediate early gene protein (cFos). The results indicated that, compared to the saline control group, 0.5 mg/kg ketamine significantly increased the number of cFos-positive cells in the medial prefrontal cortex, intermediate lateral septal nucleus, and periaqueductal gray matter. Conversely, 15 mg/kg ketamine significantly increased cFos expression in the nucleus accumbens, lateral habenula, hippocampal CA3 region, amygdala, and ventral tegmental area. These findings suggest that ketamine’s activation of brain networks is dose-dependent, with different doses activating distinct brain regions. This study provides a foundation for investigating the neuropharmacological effects of different ketamine doses and investigating brain regions associated with its antidepressant and addictive properties.

Abstract:

In order to better monitor the health state of power lithium batteries. A lithium battery health state prediction method based on Improved Bidirectional Temporal Convolutional Network, Long Short Term Memory Network and Attention Mechanism is proposed. The hyperparameters of the proposed method are optimized using Crested Porcupine Optimizer. Tests were conducted on the University of Maryland lithium battery charge/discharge dataset to extract capacity-related health features, and the health features with higher correlation were screened by Pearson correlation coefficient as inputs to the neural network algorithm. The Root Mean Squard Error of the proposed method is no more than 0.020, the Mean Absolute Error is no more than 0.017, and the R-Square is above 0.995 for all battery health state predictions. Higher accuracy can be achieved in lithium battery health state prediction.

Abstract:

As the internal electrode materials in multilayer ceramic capacitors (MLCCs) are increasingly being replaced by base metals such as nickel instead of precious metals, the sintering process must be conducted in a reducing atmosphere. This work investigates the properties of Mn-doped BaTiO3-based ceramics sintered under different reducing atmospheres, exploring the effect of varying H?/N? ratios on dielectric performance and reliability. BaTiO3-based ceramics were prepared using the solid-phase method under different reducing atmospheres. X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) were employed to characterize the microstructure and properties of the samples. The study found that the polarization mechanism significantly affects the dielectric performance and reliability of BaTiO3-based ceramics. Specifically, as the reducing atmosphere intensifies, the polarization mechanism transitions from short-range defect dipole polarization to long-range defect carrier polarization, impacting the dielectric constant, dielectric loss, and insulation resistance. The sample sintered under 1.5% H?/98.5% N? (S2) exhibited the highest dielectric constant, lowest dielectric loss, and best insulation resistance, demonstrating excellent overall performance. Additionally, BaTiO3-based MLCCs with thickness of 0.9 μm and the TCC of X7R were successfully fabricated. This work provides theoretical and technical guidance for improving the dielectric performance and reliability of BME-MLCCs.

Abstract:

3D scene reconstruction is a critical research topic in autonomous driving, robotics, and related fields, with extensive applications in navigation mapping, environmental interaction, and virtual/augmented reality tasks. Current deep learning-based reconstruction methods can be primarily categorized into five groups from the perspectives of scene representation and core modeling techniques: cost volume-based depth estimation methods, truncated signed distance function (TSDF)-based voxel approaches, transformer architecture-based large-scale feedforward methods, multilayer perceptron (MLP)-based neural radiance fields (NeRF), and 3D Gaussian Splatting (3DGS). Each category exhibits unique strengths and limitations. The emerging 3DGS method distinguishes itself by explicitly representing scenes through Gaussian functions while achieving rapid scene rendering and novel view synthesis via efficient rasterization operations. Its most significant advantage lies in diverging from NeRF''s MLP-based scene representation paradigm - 3DGS ensures both efficient rendering and interpretable editable scene modeling, thereby paving the way for accurate 3D scene reconstruction. However, 3DGS still faces numerous challenges in practical scene reconstruction applications. Based on this analysis, this paper first provides a concise introduction to 3DGS fundamentals and conducts comparative analysis with the aforementioned four categories. Following a systematic survey of existing 3DGS reconstruction algorithms, we summarize the key challenges addressed by these methods and review current research progress on core technical difficulties through representative case studies. Finally, we prospect potential future research directions worthy of exploration.

Abstract:

The study investigates the influence of different raw material particle sizes of barium titanate (BaTiO?, BT) on the dielectric properties of ceramics and aims to optimize their electrical performance through doping modifications. Ceramic samples were prepared using BT powders with particle sizes of 100 nm, 150 nm, 200 nm, and 250 nm via the solid-state ball milling method. Y?O?, Ho?O?, MgO, and SiO? were introduced as dopants to control grain growth and tailor the dielectric properties. The microstructure and dielectric characteristics of the ceramic samples were systematically analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and dielectric measurements. The results revealed that the BT-10 sample exhibited significant dopant diffusion, which hindered the formation of an ideal "core-shell" structure and reduced tetragonality. While BT-10 showed a relatively high dielectric constant, its temperature stability was poor. In contrast, the BT-15, BT-20, and BT-25 samples successfully formed a "core-shell" structure, with BT-25 demonstrating the highest tetragonality. The BT-25 sample exhibited the most favorable electrical performance, including the highest saturation polarization strength (Ps = 11.817 μC/cm2), remanent polarization strength (Pr = 1.465 μC/cm2), and superior dielectric stability under DC bias conditions. These findings indicate that the BT-25 sample offers the most balanced and optimized overall performance. Furthermore, the study highlights the challenges associated with using smaller BT particle sizes in fabricating "core-shell" structured ceramics, particularly the increased specific surface area and higher defect density of BT powders. This research provides valuable theoretical insights and technical guidance for optimizing the dielectric layers of multilayer ceramic capacitors (MLCCs).

Abstract:

Hand pose estimation based on RGB images shows crucial application prospects in the fields of dynamic gesture recognition and human-computer interaction. However, existing methods face numerous challenges. For example, the high degree of self-similarity of the hand and the extremely dense distribution of key points make it extremely difficult to achieve high-precision prediction under the condition of low computational cost, which in turn leads to limitations in performance in complex scenarios.In view of this, this paper proposes a two-dimensional (2D) hand pose estimation model based on the YOLOv8 network, namely FAR-HandNet. This model ingeniously integrates the Focused Linear Attention module, the key point alignment strategy, and the regression residual fitting module, effectively enhancing the feature capture ability for small target areas (such as the hand), while reducing the adverse impact of self-similarity on the positioning accuracy of hand key points. It is worth mentioning that the regression residual fitting module uses a flow-based generative model to fit the distribution of key point residuals, which greatly improves the accuracy of the regression model.The experiments in this paper are carried out on the CMU and FreiHAND datasets. The experimental results clearly show that FAR-HandNet has obvious advantages in terms of the number of parameters and computational efficiency, and performs excellently in PCK (Percentage of Correct Keypoints) under different thresholds, showing a significant improvement compared with existing methods. In addition, the inference time of this model is only 32ms. The ablation experiments further confirm the effectiveness of each module, fully verifying the effectiveness and superiority of FAR-HandNet in the hand pose estimation task.

Abstract:

The loss of upper limb function brings a lot of inconvenience to the life of amputees. In order to improve the life quality of upper limb amputees, it is necessary to develop a low-cost, lightweight and powerful prosthetic system. In this paper, a three-degree-of-freedom modular light-weight upper limb prosthetic arm and its multi-joint cooperative control system are designed to provide a lightweight, economical, modular and comprehensive prosthetic solution. By using the hollow structure design, the overall weight of the prosthetic arm is significantly reduced (about 2 kg), which is much lower than existing commercial prosthetic products, while ensuring the number of degrees of freedom and effectively reducing manufacturing costs, improving the comfort and suitability of the prosthetic arm. In addition, the multi-joint control system designed in this paper, combined with the precise coordination algorithm, can accurately control each joint to reach a predetermined Angle at the same time, to meet the needs of different degrees of amputees for multi-joint cooperative movement. Through precision and efficiency tests, the results show that the prosthesis performs well in terms of control accuracy, and the motion efficiency can meet most of the needs of daily life.

Abstract:

Currently, with the exponential growth of data on the internet, the complexity of big data processing systems has also increased dramatically. To adapt to changes in factors such as cluster resources, datasets, and applications, big data processing systems provide adjustable configuration parameters tailored to different application scenarios. Among these systems, Spark is one of the most popular and contains over 200 configuration parameters for controlling parallelism, I/O behavior, memory settings, and compression. Incorrect configuration of these parameters often leads to severe performance degradation and stability issues. However, both ordinary users and expert administrators face significant challenges in understanding and tuning these settings for optimal performance, resulting in substantial human and time costs. In the tuning process, selecting unreasonable parameter ranges can increase time costs by fivefold, or even worse, cause operational failures in the cluster and terminate system operation—an incalculable loss for large-scale clusters serving customers.

Abstract:

Electromagnetic pulse sound source (Boomer) is a commonly used explosion sound source in marine seismic exploration, and the deep-sea application of such explosion sound source needs to solve cavitation suppression problem. In this paper, a deep-sea boomer source based on pressure compensation balance is proposed. A boomer transducer with a maximum working pressure of 20MPa is developed and tested in a high-pressure anechoic tank. Through the analysis of the hydrophone outputs under different energy and pressure levels, it can be seen that an air sac with the initial pressure of 0.5MPa can effectively balance the internal and external pressure of the transducer, solve the problem of cavitation suppression, and realize the excitation of broadband pulse sound waves. The repeatability of the acoustic wave is very good, and the minimum correlation coefficient is to 0.986. With the increase of working pressure from 0.5MPa to 20MPa, the main change in acoustic characteristics is the amplitude attenuation (204.6dB to 194.2 dB) and width compression (182μs to 88μs), and the main frequency (2.3kHz as the center) slightly shifted to high frequency. Compared with the hydrophone output in the process of pressure rising and downing in the high-pressure anechoic tank, it can be seen that the repeatability of the acoustic wave is better. The higher the pressure, the better the waveform consistency, indicating that the boomer transducer based on pressure compensation balance has a more stable performance under high pressure environment.

Abstract:

Cancer is a genetically related disease with multiple subtypes, each exhibiting significant differences in genetics, phenotype, and treatment response. Accurate classification of cancer subtypes is critical for personalized treatment, as it helps improve therapeutic outcomes. However, cancer subtype classification methods based on patient gene expression data often struggle to effectively distinguish rare subtypes in the presence of imbalanced samples. To address this issue, a cancer subtype classification method called MFP-VAE (Meta-learning Few-shot Prototype learning VAE) is proposed, focusing on handling datasets with imbalanced samples. This method improves the sampling strategy to ensure balanced consideration of different subtypes in meta-learning tasks. The model employs a variational autoencoder for feature extraction and classifies samples by calculating the distance between the samples and the subtype prototypes. Experimental results show that MFP-VAE outperforms existing methods on two public cancer datasets, significantly improving classification accuracy, especially under imbalanced sample conditions. Furthermore, survival analysis reveals that the distinguished cancer subtypes exhibit significant differences in clinical characteristics, providing meaningful clinical insights.

Abstract:

With the rapid development of deep learning technology, autism screening based on neural signals such as Electroencephalography (EEG) is gradually emerging as a novel diagnostic approach. However, due to the complexity of EEG data acquisition, especially for children, insufficient data often poses a challenge. Data augmentation methods are commonly used to address the scarcity of real-world data, with Generative Adversarial Networks (GANs) being a frequently applied technique. However, due to the limited scale and diversity of data, current augmentation methods have yet to achieve optimal classification performance. This study introduces an improved conditional diffusion model to enhance both raw EEG signals and their corresponding functional connectivity temporal graphs. Experimental results demonstrate that this method significantly improves autism classification performance, achieving maximum classification accuracies of 84.38% and 79.01% for resting-state and task-state data, respectively. These findings validate the effectiveness of data augmentation based on the conditional diffusion model in enhancing autism screening outcomes.

Abstract:

Existing indoor three-dimensional (3D) object detection is able to detect a limited number of object categories, thus limiting the application on intelligent robotics. Open vocabulary object detection is able to detect all objects of interest in a given scene without defining object categories, thus solving the shortcomings of indoor 3D object detection. At the same time, the large language model with prior knowledge can significantly improve the performance of visual tasks. However, existing researches on open-vocabulary indoor 3D object detection only focuses on object information and ignores contextual information. The input data for indoor 3D object detection is mainly point cloud, which suffers from sparsity and noise problems. Relying only on the object point cloud can negatively affect the 3D detection results. Contextual information contains scene information, which can complement the object information to promote the recognition on object category. For this reason, this paper proposes an open vocabulary 3D object detection algorithm based on contextual information assistance. The algorithm integrates contextual information and object information through a large language model, and then performs chain-of-thought reasoning. The proposed algorithm is validated on SUN RGB-D and ScanNetV2 datasets, and the experimental results show the effectiveness of the proposed algorithm.

Abstract:

The high-pressure anechoic tank is an important experimental testing platform for the development of deep-sea transducers, sensors, and other acoustic instruments and equipment. In this paper, background noise and acoustic field fluctuations at different frequencies were measured for the homemade 20MPa high-pressure anechoic tank. The echo interference level under fixed measurement position and distance conditions was calculated, and the echo interference curve was drawn. The time-frequency characteristics of signals under typical low-frequency and high-frequency conditions were analysed. The measurement results of background noise show that although the background noise inside the tank is relatively high and has characteristic peaks in the frequency range of 10-12kHz, it allows for measurement experiments with sufficient signal-to-noise ratio conditions. Meanwhile, the time-domain waveform results of sound field fluctuations measured in different frequencies show that the signal amplitude rapidly decays after a transmission width of 2ms, and the higher the frequency, the faster the attenuation, indicating that the sound absorption cone inside the tank has a good sound absorption effect. The calculation results of echo interference level show that most frequency points above 10kHz do not exceed ± 1dB. The designed fixed measurement position meets the requirements of free field testing, especially the echo interference of frequency points such as 20kHz, 28kHz, and 34kHz does not exceed ±0.5dB, which meets the requirements of precision measurement.

Abstract:

The reconstruction of cerebral blood flow velocity (CBFV) is essential for the long-term assessment of cerebrovascular function. To this end, this study proposes a multivariate time-series model based on a Transformer encoder to reconstruct CBFV signals. The model utilizes time-series signals of arterial blood pressure (ABP) and CO2 to achieve accurate CBFV reconstruction. A long short-term memory (LSTM) module is introduced into the model to address the limitation of dispersed global attention in the attention mechanism, thereby enhancing the processing of local details. Additionally, a mixed loss function is employed to control local waveform errors, improving reconstruction accuracy. Furthermore, a transfer learning strategy is designed based on the correlation between ABP and electrocardiogram (ECG) signals to alleviate the impact of data scarcity on the reconstruction task. Experimental results on the cerebrovascular regulation dataset of diabetic patients from Beth Israel Deaconess Medical Center demonstrate that the proposed model outperforms existing regression models and deep learning models in CBFV reconstruction tasks. The results show a Pearson correlation coefficient of 0.518, a dynamic time warping distance of 17.879, and a mutual information value of 0.343 between the reconstructed and true values. Additionally, the model can reconstruct 200 data points within 0.04 seconds.

Abstract:

Based on the path planning method that ensures tangency between arcs and straight lines, potential collision conditions during parking are analyzed. Moreover, parking path planning for oblique spaces is conducted, with obstacles and parking space boundaries evaluated in detail to ensure the reasonableness of the planning. Additionally, a collision constraint model for the parking path is constructed to account for the dynamic characteristics and geometric constraints of vehicle motion. Distance equations for critical collision conditions are developed to determine safe distances and effective areas during parking maneuvers. Simulation experiments conducted under various working conditions demonstrate that collision avoidance is effectively achieved through the path planning method, which aligns with the vehicle"s traveling direction and accommodates varying lane widths. Ultimately, the validity of the planned paths is verified, and the safety and adaptability of these paths are assessed to ensure secure parki

Abstract:

Chiplet-based multi-chip integration designs provide a flexible and scalable solution that surpasses traditional SoC (System on Chip) monolithic integration. However, inter-chiplet communication has become a significant bottleneck affecting overall system performance. The Network on Interposer (NoI) plays a pivotal role in multi-chip systems, directly influencing both performance and development costs. In this paper, we review NoI communication topologies for heterogeneous chiplets. We thoroughly explore the importance of current inter-chiplet communication architectures and discuss their design and implementation methods. This paper covers the entire communication process, spanning from protocol and interface layers to the application layer, classifying interconnect topologies based on their structural configurations and providing in-depth analyses and cross-comparisons for each category. Furthermore, we investigate future directions in NoI communication technologies, identifying technical challenges and potential solutions. We also propose advanced evaluation methods and modeling techniques for reusable interposer layers and topologies. This review aims to provide researchers with a thorough understanding of the current landscape and future trends in NoI technology, emphasizing its crucial role in advancing next-generation semiconductor devices across a wide spectrum of applications.

Abstract:

In the era of big data, the storage of massive amounts of data has become a challenging problem. DNA storage technology, as a cutting-edge solution to this challenge, particularly focuses on the development and challenges of information editing technology. Initially, DNA storage primarily served "cold" data, but the latest advancements in the technology have driven its development towards supporting data updates and management for more advanced applications. This paper proposes an incremental management method for secure DNA storage, designing a hybrid encryption mechanism that supports multi-party editing and a DNA incremental storage model. While ensuring security, this model achieves secure and efficient information editing and management under existing technological constraints through a partitioned storage scheme and efficient indexing encoding. This approach meets the modern data management requirements for flexibility and cost-effectiveness, providing new perspectives and strategies for addressing core issues in DNA data management.

Abstract:

To enable autonomous driving systems to effectively detect and locate road potholes in complex environments, improvements have been made to the existing YOLOv5 object detection algorithm. Firstly, MobileNetV3 is employed to replace the original backbone of the model, reducing the parameter count and achieving a more lightweight network design. Additionally, a BiFPN (Bidirectional Feature Pyramid Network) module is introduced in the neck of YOLOv5, significantly enhancing the model"s performance in multi-scale feature fusion, information propagation, feature representation, and detection accuracy, while maintaining the lightweight nature of the architecture. Furthermore, the concept of image style transfer from Generative Adversarial Networks (GANs) is incorporated, utilizing PaddleGAN for data augmentation to increase the diversity of the dataset. Finally, experiments conducted on a custom dataset revealed that the improved YOLOv5 algorithm achieved a 3.5% increase in accuracy, a 0.9% improvement in mAP, and an enhancement in detection speed by 5.8 frames per second (fps). The proposed algorithm is more lightweight and enhances detection precision, providing a valuable technical reference for pothole detection in complex environments.

Abstract:

This article introduces a modular design concept that can realize the general observation of marine environment and multi -equipment integration of the universal marine buoyant data collection system. This system uses ARM chip timer and interrupt controller to virtually collects multi -module parallel collection processing circuits. The full system is divided into three modules: meteorological security, hydrotoma and communication according to the function of marine data buoyoma functions, and realizes the continuous collection and processing of buoyant multi -equipment, as well as real -time two -way communication. The serial extension chip realizes the system interface expansion, which improves the system"s installation capacity from the hardware. The communication module uses DMA technology to realize the functions of real -time data of dual -road real -time data, which realizes the reliable and safe operation of the buoyant system on the sea, and also improves its human -computer interaction function. This system has verified the stability, reliability, and measurement accuracy of the system through laboratory testing and operation experiments at sea.

Abstract:

LOGO is a new distributed computing framework using a Non-MapReduce computing paradigm. Under the LOGO framework, big data distributed computing is completed in two steps. The LO operation runs a serial algorithm in a number of nodes or virtual machines to process independently the random sample data blocks, generating local results. The GO operation uploads all local results to the master node and integrate them to obtain the approximate result of the big data set. The LOGO computing framework eliminates data communication between nodes during iterations of the algorithm, greatly improving computing efficiency, reducing memory requirements, and enhancing data scalability. This article proposes a new distributed machine learning algorithm library RSP-LOGOML under the LOGO computing framework. A new distributed computing is divided into two parts: the serial algorithm executed by the LO operation and the ensemble algorithm executed in the GO operation. The LO operation can directly execute existing serial machine learning algorithms without the need to rewrite them according to MapReduce. The GO operation executes ensemble algorithms of different kinds depending on the ensemble tasks. In this article, the principle of LOGO distributed computing is introduced first, followed by the algorithm library structure, the method for packaging existing serial algorithms and the ensemble strategy. Finally, implementation in Spark, App development, and the results of performance tests for various algorithms are demonstrated.

Abstract:

Nowadays, with the rapid development of the Internet, more and more new applications appear gradually, the scale of network expand constantly, and the architecture of network is more and more complicated. As one of the basic technologies for enhancing network controllability, traffic classification can not only provide better QoS for ISPs, but also supervise and manage network effectively, which can ensure the security of the Internet. In this paper we review the research methods and achievements in the field of traffic classification, compare these traditional methods, and point out their advantages and disadvantages. On the other hand, for the real challenges of real-time classification of high-speed network environment, encrypted traffic classification, fine-grained traffic classification, and dynamically changed protocols classification, we describe and analyze the related research progress. Finally, we look ahead the future research direction.

Abstract:

Repetitive sequences are prevalent in genomes. A large number of experiments have confirmed that they play an important role in biological evolution. At present, the discovery and detection of the repetitive sequences have been becoming a hot topic of genomics. This paper summarizes the research progress in this regard, and briefly analyses the associated tools. Finally, the development of repetitive sequences in future is prospected.

Abstract:

The technology of the robot represents a national high-tech level and the degree of automation. It is helpful to develop the industry of service robots in China if we know the current situation and development trend of service robots research clearly. Recently, robotic cleaners and educational robots have been in great demand. Entertainment robots and surveillance robots are developed rapidly and the market expands quickly. Medical robots begin to enter the modern life and have played an important role in the modern surgery. To satisfy the great market and shorten the distance between China and developed countries, it is necessary to capture the development trend of the technology of service robots. R&D on service robots should focus on the integrated technologies on intelligence, modularization and network.

Abstract:

The human gut is densely populated by the gut microbiota. There are accumulating evidences indicating that the gut microbiota plays a significant role in the function of the body, which including the metabolism and energy absorption, the development in the function of gastrointestinal, the modulation of immune system and so on. Many chronic diseases, such as obesity, obesity-associated inflammation, inflammatory bowel disease and depression, are related to gut microbiota dysbiosis. The research of the interaction between intestinal bacteria and human body is instructive to the prevention or treatment of many chronic diseases and maintaining health.

Abstract:

The accurate contour delineation of the target and organs at risk (OAR) is essential in treatment planning for image guided radiation therapy. In clinical applications, the contour delineation is often done manually by clinicians, which may be accurate, but time-consuming and tedious for users. Although a lot of automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. In this work, a novel approach of target delineation was proposed. Target delineation of OARs was achieved by using snake model and multiscale curve editing to obtain promising results. It allows users to quickly improve contours by a simple mouse click. Experimental results demonstrate the effectiveness of the proposed method for clinical target delineations.

Abstract:

Gene is the genetic material basis. All life phenomena, like disease and death, are related to Gene. Gene sequencing is a way to read life. With the development of new generation high-throughput sequencing technology, TB or more sequence data will be generated daily. It’s more difficult to interpret these big and complex data than to acquire them. Sequence data interpretation is a critical step in current biological research and has great practical significance. It’s a great challenge for current computer systems and computing models to store, process and analysis massive high throughput sequence data. With survey, especially from BGI (Beijing Genome Institute), the current status, problems and measures taken to process high throughput sequence data will be discussed. However, the challenge is too big to be solved unless more people in different fields work together in depth for a long term.

Abstract:

This paper gives a comprehensive introduction to the status of current machine translation research and technology, and analyzes the key problems to be resolved. Finally our idea of the future trends and prospects of machine translation are put forward.

Abstract:

As an advanced biosensing strategy, the electrochemical biosensor is made up of the active sensing biomaterial and the electrochemical signal transducer, and is being widely applied in the fields of clinical medicine, drug and food analysis as well as the environmental monitoring. The electrochemical biosensor has the advantages of excellent specificity, high sensitivity and simplicity. This review focused on the fundamental principle of electrochemical biosensor, its classification and the biomedical applications. The prospect of the electrochemical biosensors was outlined as well.

Abstract:

With the increasing concerns of global warming and resource constraints, electric vehicles (EVs) have made great progress during the past decade. The electric driving system of EVs has dinstinct advantages, such as quick response, easy measurement , and precise control of motor torque, available flexible driving architecture, and regenerative braking, etc. Such advantages can be used to improve the performance of vehicle dynamic control. This paper presents the recent research efforts on electric vehicle dynamic control in terms of parameters estimation and dyanmic control scheme and methodology, especially focusing on the tire-road friction estimaion , novel traction control methods. The lateral dynamic control including the electrical differential control, direct yaw moment control, and the integratin chassis cotrol is proposed. Several prospects for vehicle dynamic control are proposed.

Abstract:

Transcranial magnetic stimulation (TMS) is a non-invasive technique that can be used for brain studying and clinical therapy. Firstly, the technology feature and application of the TMS instrument were introduced. Then several TMS coil positioning methods were evaluated and several key problems about TMS coil positioning were discussed. The aim of this study was to propose a new method for TMS coil positioning. The new method combines three aspects of quantitative information including the brain scalp, brain anatomy and brain function and has great advantages and broad application prospects.

Abstract:

Automatic drive is an important application field of artificial intelligence. In this paper, a novel training strategy for self-driving vehicles was investigated based on the deep reinforcement learning model. The proposed method involves a Q-learning algorithm with filtered experience replay and pre-training with experiences from professional drivers, which accelerates the training process due to reduced exploration spaces. By resampling the input state after clustering, generalization ability of the strategy can be improved due to the individual and independent distribution of the samples. Experimental results show that, in comparison with conventional neural fitted Q-iteration algorithm, the training efficiency and controlling stability can be improved more than 90% and 30% respectively by the proposed approach. Experimental results with more complex testing tracks show that, average travel distance can be improved more than 70% in comparison with the Q-learning algorithm by the proposed method.

Abstract:

Hadoop job schedulers typically use a single resource abstraction and resources are allocated at the level of fixed-size partition of the nodes, called slots. These job schedulers ignore the different demands of jobs and fair allocation of multiple types of resources, leading to poor performance in throughput and average job completion time. This paper studies and implements a Muti-resource Fair Scheduler (MFS) in Hadoop. MFS adopts the idea of Dominant Resource Fairness (DRF). It uses a demand vector to describe demands for resources of a job and allocates resources to the job according to the demand vector. MFS uses resources more efficiently and satisfies multiple jobs with heterogeneous demands for resources. Experiment results show that MFS has higher throughput and shorter average job completion time compared to Hadoop slot-based Fair Scheduler and Capacity Scheduler.

Abstract:

Wireless Capsule Endoscope (WCE) is a very promising tool for the examination of the gastrointestinal (GI) tract. However, there are some problems to be solved for the existed WCE, and one key problem is the accurate localization and tracking of the WCE. Among the possible localization methods, the magnet-based localization technique has its advantages: no need for power, not much space occupation, continuously tracking ability, and no negative effect. In this paper, we present the localization method for the magnet objective inside the WCE based on the magnetic sensor array outside the human body. Through the algorithm and system design we realize real time tracking of 3D position and 2D orientation of the magnet based on the magnetic dipole model. In order to overcome the interference of the human body movement, we propose the multi-magnets’ localization method; also, the 3D positioning and 3D orientation method is proposed, which can be used to make the 3D recovery of the GI tract and the accurate computation of the physiological tissue parameters. The real experiments show that the proposed localization system can run well and obtain the accuracy with 2~3mm for the magnet.

Abstract:

For the advantages of noninvasive, real-time and quantitative detection, ultrasonic transient elastography has important clinical application value. This work investigates the transient elastography in a few ways and aims to design a transient imaging system. The Displacement tracking algorithm based on correlation techniques and the parabolic interpolation algorithm is proposed to improve the accuracy. A novel match filter is designed to convolute with the estimated displacement in the time direction to boost the SNR of the displacement for a better strain image mapping. The convoluted result shows the match filter can significantly improve the strain image quality and help getting more accurate Youngs modulus estimation. The Time Gain Compensation (TGC) circuit is designed to compensate the attenuated power of the ultrasound signal. And a modified polyacrylamide gel based tissue-mimicking phantom is also developed in this paper, both indentation testing and transient elastography are used to characterize the elastic properties of this phantom. The results are almost consistent with each other.

Abstract:

This paper introduces the scientific computing grid, ScGrid, and it’s middleware SCE. ScGrid is built as one virtual supercomputer, integrating computing resource from more than 30 institutes. It provides unified,?easy to use and reliable scientific computing services. SCE is a lightweight grid middleware, which supports global job scheduling and unified data view. It provides multiple user interfaces including command line, grid portal and APIs. At present, ScGrid has been very successfully used in Chinese Academy of Sciences and widely accepted by more than 200 users.

Abstract:

Effectively reducing power line interference is always an important issue in electromyography (EMG) signal recordings and analysis. In this study, four commonly used de-noising methods, including digital notch, LMS based adaptive filter, Kalman filter and S transform, which may be suitable for the reduction of power line interference in real-time EMG recordings, were chosen and their performance in reducing the power line interference from EMG signal recordings were quantitatively analyzed and compared. The pilot results of this study showed that Kalman filter presented the best whole performance in attenuating power line interference from EMG signals and S transform de-noising method illustrated the best performance when the power line interference was severe.

Abstract:

With the rapid increase in numbers and scales of deep web sites on the Internet, search for data or information from deep web sites by submiting queries to and obtaining results from the backend databases has become a major means in information retrieval from the Web. This area has attracted many researchers to devote their efforts on development of technologies to make better use of information in th deep web. One challenge is searching for and integration of data from various databases in deep web. Since deep web is dominated by text data, research and development of technologies for text information retrieval from deep web have a broad application potential. In this paper, we review the state-of-the-art of deep web research in details and propose some future research directions.

Abstract:

Several of the top ranked supercomputers are based on the hybrid architecture consisting of a large number of CPUs and GPUs. High performance has been obtained for problems with special structures, such as FFT-based imaging processing or N-body based particle calculations. However, for the class of problems described by partial differential equations (PDEs) discretized by finite difference (or other mesh based methods such as finite element) methods, obtaining even reasonably good performance on a CPU/GPU cluster is still a challenge. In this paper, we propose and test an hybrid algorithm which matches the architecture of the cluster. The scalability of the approach is implemented by a domain decomposition method, and the GPU performance is realized by using a smoothed aggregation based algebraic multigrid method. Incomplete factorization, which performs beautifully on CPU but poorly on GPU, is completely avoided in the approach. Numerical experiments are carried out by using up to 32 CPU/GPUs for solving PDE problems discretized by FDM with up to 32 millions unknowns.

Abstract:

Foot drop is the inability to voluntarily dorsiflex the ankle during the swing phase of gait and is usually caused by weakness and damages of the peroneal nerve. The consequences of the foot drop include the decreasing of gait quality, the limiting of mobility, the increasing of falling risk, and great increasing of energy expenditure during walking. Firstly biosignal sensors are used in the drop foot stimulator to detect foot movements. Then the surface drop foot stimulator produces a predefined stimulation profile to the peroneal nerve or tibialis anterial to elicit a dorsiflexion of the foot synchronized with the swing phase of gait to lift the foot. This paper reviewed the fundamentals and current researches of drop foot stimulators. Moreover, the development trends of the closed loop drop foot stimulator were also discussed in the paper.

Abstract:

Human action recognition acts as an important role in human machine interaction. This paper proposes a human body recognition method from depth image based on part size and position features. Random forest classifiers are trained with different parameters. Experimental results demonstrate the feasibility of proposed approach. Recognition accuracy is about 91% and the computation time is about 0.96 us per pixel.

Abstract:

Random Forests is an important ensemble learning method and it is widely used in data classification and nonparametric regression. In this paper, we review three main theoretical issues of random forests, i.e., the convergence theorem, the generalization error bound and the out-of-bag estimation. In the end, we present an improved Random Forests algorithm, which uses a feature weighting sampling method to sample a subset of features at each node in growing trees. The new method is suitable to solve classification problems of very high dimensional data.

Abstract:

In order to help the hearing loss children, we obtained hearing loss children’s fallible pronunciation texts and the confusing pronunciation text pairs form a good deal of hearing loss children’s audio pronunciation data. We designed a data-driven 3D talking head articulatory animation system, it was driven by the articulatory movements which were collected from a device called Electro-magnetic articulography (EMA) AG500, the system simulated Chinese articulation realistically. In that way, the hearing loss children can observe the speaker’s lips and tongue’s motions during the speaker pronouncing, which could help the hearing loss children train pronunciation and correct the fallible pronunciations. Finally, a perception test was applied to evaluate the system’s performance. The results showed that the 3D talking head system can animate both internal and external articulatory motions effectively. A modified CM model based synthesis method was used to generate the articulatory movements. The root mean square between the real articulatory movements and synthetic articulatory movements was used to measure the synthesis method, and an average value of RMS is 1.25 mm.

Abstract:

Morphological parameter measurement of Pseudosciaena Crocea plays an important role in its genetic selection and quality improvement. In this paper, an automatic detecting system which can measure the Pseudosciaena Crocea morphological parameters such as weight, length and body width was developed based on the machine vision and weighing sensor technology. The system can automatically detect the external morphology parameters by the machine vision, and get weight parameters through the weighing sensor. The mean errors of dimensional measurement and weighting are 0.28% and 0.74% respectively, which shows that the developed system can completely meet the requirements of morphological parameter measurement for Pseudosciaena Crocea. It is a new effective method to the automatic detection of fish morphology parameters.

Abstract:

In this work the thermal behavior of the LiNi1/3Co1/3Mn1/3O2 cathode material for soft packed lithium-ion power batteries during charging and discharging at different C-rate were conducted using the ARC (accelerating rate calorimeter) to provide an adiabatic environment. The overall heat generated by the lithium-ion battery during use, is partly reversible and partly irreversible, due to entropy change and joule heating, respectively. It indicates that the heating generation of lithium-ion cell is decided by the C-rate of charge and discharge. The heat is smaller at low C-rate of charge and discharge. For example, the heating generation of battery increases 7.16℃ at 0.2C-rate and the entropy change heat is clearly embodied. The joule heating is more remarkable than the entropy change during charging and discharging at high C-rate. For instance, the heating generation of cell increased 25.63℃ at 1C-rate. The heat generation of charge is less than discharge at the same C-rate. The DC inter insistence of cell at the SOC (State of Charge) of 0 to 10% increases suddenly, so the heating generation power will reach its maximum in this period during discharge. It is valuable for the design of heat dissipation in lithium-ion battery thermal management.