Recent years have witnessed growing interest in leveraging the bidirectional wave control of intelligent omni-surfaces (IOS) for integrated sensing and communication (ISAC) systems. Nevertheless, acquiring precise channel state information (CSI) is particularly challenging due to the inherent interplay between the electromagnetic properties of IOS and the dual functions of ISAC. In this paper, we propose a robust beamforming design for IOS-enabled ISAC systems. We jointly optimize the transmit beamforming, sensing waveform and IOS phase shifts to minimize the Cram´er-Rao bound (CRB) for sensing while ensuring communication reliability under an outage probability constraint. The resulting mixed-integer non-convex problem is tackled via a dual-loop penalty dual decomposition (PDD) algorithm. This framework solves the augmented Lagrangian (AL) subproblem in the inner loop, while the outer loop adjusts dual variables and penalty parameters to enforce constraint satisfaction. Simulation results demonstrate that our design substantially enhances sensing accuracy and communication reliability in scenarios with large CSI errors or fluctuating service requirements. Furthermore, it is shown that an optimal ratio between sensing and passive IOS elements must be maintained to balance energy utilization and spatial sampling capability in ISAC systems.

Scoliosis significantly impacts quality of life, highlighting the need for effective early scoliosis screening (SS) and intervention. However, current SS methods often involve physical contact, undressing, or radiation exposure. This study introduces an innovative, non-invasive SS approach utilizing a monocular RGB camera that eliminates the need for undressing, sensor attachment, and radiation exposure. We introduce a novel approach that employs Parameterized Human 3D Reconstruction (PH3DR) to reconstruct 3D human models, thereby effectively eliminating clothing obstructions, seamlessly integrated with an ISANet segmentation network, which has been enhanced by Multi-Scale Fusion Attention (MSFA) module we proposed for facilitating the segmentation of distinct human trunk and limb features (HTLF), capturing body surface asymmetries related to scoliosis. Additionally, we propose a Swin Transformer-enhanced CMU-Pose to extract human skeleton features (HSF), identifying skeletal asymmetries crucial for SS. Finally, we develop a fusion model that integrates the HTLF and HSF, combining surface morphology and skeletal features to improve the precision of SS. The experiments demonstrated that PH3DR and MSFA significantly improved the segmentation and extraction of HTLF, whereas ST-based CMU-Pose substantially enhanced the extraction of HSF. Our final model achieved a comparable F1 (0.895$\pm$0.014) to the best-performing baseline model, with only 0.79% of the parameters and 1.64% of the FLOPs, achieving 36 FPS-significantly higher than the best-performing baseline model (10 FPS). Moreover, our model outperformed two spine surgeons, one less experienced and the other moderately experienced. With its patient-friendly, privacy-preserving, and easily deployable solution, this approach is particularly well-suited for early SS and routine monitoring.

Electroencephalogram (EEG) signals provide rich spatiotemporal brain features crucial for epilepsy diagnosis. Though traditional deep neural networks combining attention mechanisms excel in feature extraction, these models do not fully emulate the neural processing efficiency of the brain and they fall short in fully harnessing the spatiotemporal dynamics of EEG data. This study introduces the dual attention-driven and feature fusion-based spiking neural network (DAFF-SNN), which synergizes the spatiotemporal attention mechanism with SNNs' inherent temporal processing prowess to enhance the precision and efficiency for epilepsy detection. The DAFF-SNN utilizes an adaptive spiking fusion module (ASFM) for feature integration, optimizing the feature fusion process by exploiting the spatiotemporal complementarity of EEG data through a spike-driven strategy. Evaluations on the Bonn, Beriut, CHB-MIT, and Siena datasets demonstrate DAFF-SNN's high accuracies (100%, 97.6%, 98.2%, 99.6% ), achieving comparable or superior performance to state-of-the-art ANN methods, highlighting its efficient epilepsy detection capability.

Self-supervised monocular depth estimation that does not require hard-to-source depth labels for training has been widely studied in recent years. Due to its significant and growing needs, many lightweight but effective architectures have been designed for edge devices. Convolutional Neural Networks (CNNs) have shown its extraordinary ability in monocular depth estimation. However, their limited receptive field stints existing methods to reason only locally, inhibiting the effectiveness of the self-supervised paradigm. Recently, Transformers has achieved great success in estimating depth maps from monocular images. Nevertheless, massive parameters in the Transformers hinder the deployment to edge devices. In this paper, we propose MonoMixer, a brand-new lightweight CNN-Transformer architecture with three main contributions: 1) The details-augmented (DA) block employs graph reasoning unit to capture abundant local details, resulting depth prediction at a higher level of precision. 2) The self-modulate channel attention (SMCA) block adaptively adjust the channel weights of feature maps, aiming to emphasize the crucial features and aggregate channel-wise feature maps of different patterns. 3) The global-guided Transformer (G2T) block integrates global semantic token into multi-scale local features and exploit cross-attention to encode long range dependencies. Furthermore, experimental results demonstrate the superiority of our proposed MonoMixer both at model size and inference speed, and achieve state-of-the-art performance on three datasets: KITTI, Make3D and Cityscapes. Specifically, our proposed MonoMixer outperforms MonoFormer by a large margin in accuracy, with about 95 % fewer parameters.

While Convolutional Neural Networks (CNNs) have been the de-facto architecture for computer vision tasks, recent works have shown that self-attention-based models, such as the Vision Transformer (ViT), have achieved competitive results on various visual benchmarks compared to CNNs. More recently, MLP-Mixer replaces the self-attention layer with a spatial MLP block as a token-mixing module. It attains good scores on image classification tasks when training on large datasets with strong regularization. In this paper, we propose a Local Context MLP-like architecture (LocoMixer). Unlike MLP-Mixer, where global spatial information is captured for information exchange through token-mixing MLP, we primarily focus on the local dependencies between the patch embeddings. Our LocoMixer independently encodes the spatial features along the rows and columns of 2D image patches with fully-connected layers. Meanwhile, we apply a scale-transformation operation to learn multi-scale feature representations in our proposed multiscale token-MLP. Furthermore, we further model local cross-channel interaction by absorbing a context block into the proposed dynamic channel-MLP. When only trained on the ImageNet-1K dataset, the proposed LocoMixer achieves state-of-the-art performace compared with other MLP-like models. Moreover, the proposed LocoMixer achieves 82.3% top-1 accuracy with only 26M parameters, which is much better than most CNNs and vision Transformers under the same model size constraint. When scaling up to 90M parameters, LocoMixer achieves 84.2% top1 accuracy, which outperforms the state-of-the-art Deformable Attention Transformer (DAT).

Recently, vision Transformers (ViTs) have achieved competitive performance on many computer vision tasks. However, vision Transformers show impaired performance on small datasets when training from scratch compared with Convolutional Neural Networks (CNNs), which is interpreted as the lack of locality inductive bias. This impedes the application of vision Transformers for small-size datasets. In this work, we propose Adaptive Hybrid Vision Transformer (AHVT) as the solution to boost the performance of vision Transformers on small-scale datasets. Specifically, on spatial dimension, we exploit a Convolutional Overlapping Patch Embedding (COPE) layer to inject desirable inductive bias in model, forcing the model to learn the local token features. On channel dimension, we insert a adaptive channel features aggregation block into vanilla feed forward network to calibrate channel responses. Meanwhile, we add several extra learnable “cardinality tokens” to patch token sequences to capture cross-channel interaction. We present extensive experiments to validate the effectiveness of our method on five small/medium datasets including CFAR101100, SVHN, Tiny-ImageNet and ImageNet-lk. Our approach attains state-of-the-art performance on above four small datasets when training from scratch.

The composition and reuse of distributed design resources is the key issue in collaborative operation of complicated products. In the first place, a collaborative service framework aiding design resources reuse is established to help encapsulate reusable resources. Meanwhile, to realize interoperability between function and data of heterogeneous resources, shared ontology and private ontology are separately built for different design disciplines and tool resources within the disciplines. Then, collaborative service planning is divided into different stages. And generating algorithm serving planning program is proposed to obtain feasible program collection of service composition. Finally, taking design of aero engine compressor as an example, the effectiveness of collaborative service planning algorithm is verified by providing an atomic service ontology instance.