This study addresses the limitations of traditional truck platoon cooperative control methods in optimizing dynamic fuel efficiency. The fixed-order truck platoon has a key flaw: it is unable to dynamically respond to real-time vehicle state changes. To address this, we introduce three innovative methods based on deep reinforcement learning. Compared with fixed-cycle formation transformation strategies, the number of formation transformations is reduced to varying degrees for truck platoons of different sizes. For truck platoons with identical specifications, the impact of different cycle sizes on driving mileage is found to be minimal. This study proves that the dynamic decision mechanism based on deep reinforcement learning can effectively balance formation transformation costs and long-term fuel-saving benefits. The core value lies in establishing an intelligent control paradigm with environmental adaptability. The new algorithm significantly improves fuel economy indicators through real-time state perception and probabilistic decision-making while maintaining formation stability. This method provides a new technical route for energy-saving control in complex transportation scenarios. The core framework can be extended to multi-objective collaborative optimization fields.

Deformable image registration is crucial for medical image analysis, yet the complexity of deep learning networks often limits their deployment on resource-limited devices. Current distillation methods in registration tasks fail to effectively transfer complex deformation handling capabilities to non-iterative lightweight networks, leading to insignificant performance improvement. To address this, we propose the Framework for Iterative to Non-iterative Distillation (FIND), which efficiently transfers these capabilities to a Non-Iterative Lightweight (NIL) network. FIND employs a dual-step process: first, using recurrent distillation to derive a high-performance non-iterative teacher assistant from an iterative network; second, using advanced feature distillation from the assistant to the lightweight network. This enables NIL to perform rapid, effective registration on resource-limited devices. Experiments across four datasets show that NIL can achieve up to 60 times faster performance on CPU and 89 times on GPU than compared deep learning methods, with superior registration accuracy improvements of up to 3.5 points in Dice scores.

One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to accelerate MRI acquisition, while this could introduce artifacts and reduce signal-to-noise ratio (SNR). To accelerate CEST-MRI acquisition while maintaining suitable image quality, we proposed an attention-based multioffset deep learning reconstruction network (AMO-CEST) with a multiple radial k-space sampling strategy for CEST-MRI. The AMO-CEST also contains dilated convolution to enlarge the receptive field and data consistency module to preserve the sampled k-space data. We evaluated the proposed method on a mouse brain dataset containing 5760 CEST images acquired at a pre-clinical 3 T MRI scanner. Quantitative results demonstrated that AMO-CEST showed obvious improvement over zero-filling method with a PSNR enhancement of 11 dB, a SSIM enhancement of 0.15, and a NMSE decrease of [Formula: see text] in three acquisition orientations. Compared with other deep learning-based models, AMO-CEST showed visual and quantitative improvements in images from three different orientations. We also extracted molecular contrast maps, including the amide proton transfer (APT) and the relayed nuclear Overhauser enhancement (rNOE). The results demonstrated that the CEST contrast maps derived from the CEST images of AMO-CEST were comparable to those derived from the original high-resolution CEST images. The proposed AMO-CEST can efficiently reconstruct high-quality CEST images from under-sampled k-space data and thus has the potential to accelerate CEST-MRI acquisition.

Abstract The proliferation of high-performance personal devices and the widespread deployment of machine learning (ML) applications have led to two consequences: the volume of private data from individuals or groups has exploded over the past few years; and the traditional central servers for training ML models have experienced communication and performance bottlenecks in the face of massive amounts of data. However, this reality also provides the possibility of keeping data local for ML training and fusing models on a broader scale. As a new branch of ML application, Federated Learning (FL) aims to solve the problem of multi-party joint learning on the premise of protecting personal data privacy. However, due to the heterogeneity of devices, including network connection, network bandwidth, computing resources, etc., it is unrealistic to train, update and aggregate models in all devices in parallel, while personal data is often not independent and identically distributed (Non-IID) due to multiple reasons. This reality poses a challenge to the speed and convergence of FL. In this paper, we propose the pFedCAM algorithm, which aims to improve the robustness of the FL system to device heterogeneity and Non-IID data, while achieving some degree of federation model personalization. pFedCAM is based on the idea of clustering and model interpolation by classifying heterogeneous clients and performing FedAvg algorithm in parallel, and then combining them into personalized federated global models by inter-cluster model interpolation. Experiments show that the accuracy of pFedCAM improves 10.3% on Fashion-MNIST and 11.3% on CIFAR-10 compared to the benchmark in the case of Non-IID data. In the end, we applied pFedCAM in HomeProtect, a smart home privacy protection framework we designed, and achieved good practical results in the case of flame recognition.

With the proliferation of smart home, the research on the attack and protection methods of smart home privacy has gradually increased. Research has shown that adversaries can infer users' privacy information by analyzing smart home traffic traces. To address this problem, we investigated and summarized the existing smart home traffic obfuscation schemes. Firstly, the current situation that smart home privacy is easy to be leaked through traffic is explained. The necessity of smart home privacy data protection and challenges faced are expounded. Then, the smart home traffic obfuscation technologies are classified and summarized, including data packet filling, traffic shaping, false traffic injection, user simulation and adversarial learning. The limitations and application scenarios are analyzed. Finally, the paper holds the view that the smart home traffic obfuscation technology based on user simulation and adversarial learning is the direction with great development potential.