Robotic fish hold significant promise as efficient underwater systems, yet their inability to accurately perceive ambient flow hinders their deployment in real-world scenarios. Inspired by the natural lateral line system(LLS), a flow-responsive organ in fish that plays a crucial role in behaviors such as rheotaxis, this paper introduces the first Artificial Lateral Line System (ALLS)-based ambient flow classifier for robotic fish that allows robotic fish to perceive flow fields while swimming freely. To be specific, using just 5 pressure sensors and 3.5 minutes of swimming data, we trained a Long Short-Term Memory (LSTM) network, achieving a classification accuracy of 81.25% across 8 flow speed categories, ranging from 0.08 m/s to 0.18 m/s. A key innovation of this work is the formulation of ambient flow perception as a classification task, which not only enables the robotic fish to extract meaningful information but also enhances the robustness and generalizability of the perception framework. Extensive experiments further identify critical factors such as affecting the effectiveness of the ambient flow classifier, offering valuable insights for future development.

Robotic fish face considerable challenges in natural environment due to the absence of a comprehensive and precise model that can depict the intricate fluid-structure interactions, particularly in the presence of background flow fields. To this end, we present a novel data-driven dynamic modeling framework capable of characterizing the swimming motions of the robotic fish under various background flow conditions without the necessity for explicit flow information. The model is synthesized by an internal model with an adaptive residual acceleration model to effectively isolate and address external flow effects. Notably, the residual model employs the innovative Domain Adversarially Invariant Meta-Learning (DAIML) approach, allowing the framework to adapt to fluctuating and previously unseen background flow scenarios, enhancing its robustness and scalability. Validation through high-fidelity Computational Fluid Dynamics (CFD) simulations demonstrates the framework’s effectiveness in improving the performance of robotic fish across diverse real-world aquatic environments.

Dynamic model is essential for robust and reliable robotic fish motion control. Despite considerable efforts in robotic fish dynamic modeling, background flow has not been well considered yet, leading to the deterioration of applying robotic fish to practice. In this paper, we propose a novel dynamic model, termed Flow-Aware Robotic fish Model (FARM), that with well consideration to background flow using Koopman operators without increasing computation complexity. Specifically, we first collect motion data of the robotic fish in different background flow fields, and then obtain a linear approximation (the dynamic model) of nonlinear dynamics through carefully selected lifted functions. The obtained model can predict the next state based on the current state, control input, and average flow velocity of the local flow field. We evaluate the effectiveness of obtained model by comparing the Root Mean Square Error (RMSE) of predicted motion trajectories with real trajectories in various flow field environments. The results indicate that FARM is highly promising for obtaining a reliable dynamic model and can achieve comparable prediction accuracy even in unseen flow field environments with rough flow maps.

The study of motion control for the fish-like robots in complex fluid fields is of great importance in improving the performance of underwater vehicles, due to its strong maneuverability, propulsion efficiency, and deceptive visual appearance. In this article, a novel learning-based control framework is first proposed to autonomously explore efficient control policies that are capable of performing motion control tasks in non-quiescent and unknown background flows. First, we utilize a high-fidelity simulation system, named FishGym, to generate various uniform flows. Next, a DRL-based algorithm is incorporated with the FishGym to train the fish-like robot to control its motion to optimally complete a delicately designed task (Approaching Target and Stay) in both quiescent and uniform flow. Then, the obtained control policy together with an online estimator is directly applied to a Path-Following Task. The proposed framework well balances the simulation accuracy and the computational efficiency, which is of crucial importance for effective coupling with the learning algorithm. The simulation results indicate that, via the proposed learning framework, the robot successfully acquired a swimming strategy that can be used to adapt to different background flows and tasks. Furthermore, we also observe some adaptation behavior of the robot, such as rheotaxis, that is similar to the fish in nature, which gains us more insight into the mechanism underlying the adaptation behavior of fish in a complex environment.

Automated pancreatic cancer segmentation is highly crucial for computer-assisted diagnosis. The general practice is to label images from selected modalities since it is expensive to label all modalities. This practice brought about a significant interest in learning the knowledge transfer from the labeled modalities to unlabeled ones. However, the imaging parameter inconsistency between modalities leads to a domain shift, limiting the transfer learning performance. Therefore, we propose an unsupervised domain adaptation segmentation framework for pancreatic cancer based on GCN and meta-learning strategy. Our model first transforms the source image into a target-like visual appearance through the synergistic collaboration between image and feature adaptation. Specifically, we employ encoders incorporating adversarial learning to separate domain-invariant features from domain-specific ones to achieve visual appearance translation. Then, the meta-learning strategy with good generalization capabilities is exploited to strike a reasonable balance in the training of the source and transformed images. Thus, the model acquires more correlated features and improve the adaptability to the target images. Moreover, a GCN is introduced to supervise the high-dimensional abstract features directly related to the segmentation outcomes, and hence ensure the integrity of key structural features. Extensive experiments on four multi-parameter pancreatic-cancer magnetic resonance imaging datasets demonstrate improved performance in all adaptation directions, confirming our model's effectiveness for unlabeled pancreatic cancer images. The results are promising for reducing the burden of annotation and improving the performance of computer-aided diagnosis of pancreatic cancer. Our source codes will be released at https://github.com/SJTUBME-QianLab/UDAseg, once this manuscript is accepted for publication.

Pancreatic cancer is a lethal malignant tumor with one of the worst prognoses. Accurate segmentation of pancreatic cancer is vital in clinical diagnosis and treatment. Due to the unclear boundary and small size of cancers, it is challenging to both manually annotate and automatically segment cancers. Considering 3D information utilization and small sample sizes, we propose a model-driven deep learning method for pancreatic cancer segmentation based on spiral transformation. Specifically, a spiral-transformation algorithm with uniform sampling was developed to map 3D images onto 2D planes while preserving the spatial relationship between textures, thus addressing the challenge in effectively applying 3D contextual information in a 2D model. This study is the first to introduce spiral transformation in a segmentation task to provide effective data augmentation, alleviating the issue of small sample size. Moreover, a transformation-weight-corrected module was embedded into the deep learning model to unify the entire framework. It can achieve 2D segmentation and corresponding 3D rebuilding constraint to overcome non-unique 3D rebuilding results due to the uniform and dense sampling. A smooth regularization based on rebuilding prior knowledge was also designed to optimize segmentation results. The extensive experiments showed that the proposed method achieved a promising segmentation performance on multi-parametric MRIs, where T2, T1, ADC, DWI images obtained the DSC of 65.6%, 64.0%, 64.5%, 65.3%, respectively. This method can provide a novel paradigm to efficiently apply 3D information and augment sample sizes in the development of artificial intelligence for cancer segmentation. Our source codes will be released at https://github.com/SJTUBME-QianLab/ Spiral-Segmentation.

Pancreatic cancer is a malignant form of cancer with one of the worst prognoses. The poor prognosis and resistance to therapeutic modalities have been linked to TP53 mutation. Pathological examinations, such as biopsies, cannot be frequently performed in clinical practice; therefore, noninvasive and reproducible methods are desired. However, automatic prediction methods based on imaging have drawbacks such as poor 3D information utilization, small sample size, and ineffectiveness multi-modal fusion. In this study, we proposed a model-driven multi-modal deep learning scheme to overcome these challenges. A spiral transformation algorithm was developed to obtain 2D images from 3D data, with the transformed image inheriting and retaining the spatial correlation of the original texture and edge information. The spiral transformation could be used to effectively apply the 3D information with less computational resources and conveniently augment the data size with high quality. Moreover, model-driven items were designed to introduce prior knowledge in the deep learning framework for multi-modal fusion. The model-driven strategy and spiral transformation-based data augmentation can improve the performance of the small sample size. A bilinear pooling module was introduced to improve the performance of fine-grained prediction. The experimental results show that the proposed model gives the desired performance in predicting TP53 mutation in pancreatic cancer, providing a new approach for noninvasive gene prediction. The proposed methodologies of spiral transformation and model-driven deep learning can also be used for the artificial intelligence community dealing with oncological applications. Our source codes with a demon will be released at https://github.com/SJTUBME-QianLab/SpiralTransform.

Heterogeneous systems that contain multiple types of resources, such as CPUs and GPUs, are becoming increasingly popular thanks to the potential of achieving high performance and energy efficiency. In such systems, the problem of data mapping and communication for time-sensitive applications while reducing power and energy consumption is more challenging, since applications may have varied data management and computing patterns on different types of resources. In this paper, we propose power-aware mapping techniques for CPU/GPU heterogeneous system that are able to meet applications' timing requirements while reducing power and energy consumption by applying DVFS on both CPUs and GPUs. We have implemented the proposed techniques in a real CPU/GPU heterogeneous system. Experimental results with several data analytics workloads show that compared to performance-driven mapping, our power-efficient mapping techniques can often achieve a reduction of more than 20% in power and energy consumption.

The characterization of HTTP traffic is crucial for performance evaluation and server design. In this paper, we analyze massive Web traces generated by various busy servers in recent years, trying to find the new features of modern HTTP traffic and user behaviors. Comparing the conclusions of earlier studies with our results, we have spotted considerable unconventional ingredients in modern HTTP traffic that could hardly be described by previous models. We also propose an innovative scheme to automatically categorize these various ingredients in modern traffic. The novel aspects of our work are: (1)It reveals the sophisticated composition of modern HTTP traffic with solid evidence, (2)It provides an automatic method to analyze the composition of modern HTTP traffic and (3)It promises a powerful manner to evaluate the possible performance implication of modern HTTP traffic on existing Web servers. We hope this work would help researchers and designers to better understand new features of HTTP workloads and therefore make corresponding adaptations in design practice.