Modern power analysis attacks (PAAs) pose significant threats to hardware security, and reliably securing integrated systems against advanced PAAs has become a significant design target in integrated circuits. However, the detection accuracy of most countermeasures to PAAs significantly decreases when power side-channel information is mixed with voltage noise. In this paper, a real-time PAA detection technique is proposed to achieve high detection accuracy even with large voltage noise. The voltage drops of certain power grid (PG) nodes caused by PAA are evaluated by a number of voltage comparisons between PG nodes, which compensate for the effects of voltage noise. These voltage comparison results are analyzed by machine learning algorithms, and a linear support vector machine (SVM) model is selected as the PAA detection model. The PAAs on an IBM benchmarked microprocessor are applied to evaluate the detection accuracy, and our proposed PAA detection method achieves 93.11% accuracy in detecting a resistance of 1 Ω with noise equal to 20% of Vdd. Furthermore, the power and area overheads (evaluated using a 65-nm CMOS process) of this method are reduced by 68% and 75%, respectively, compared to those of the existing machine learning-based PAA detection techniques.

Recently, image-based 3D semantic occupancy prediction has become a hot topic in 3D scene understanding for autonomous driving. Compared with the bounding box form of 3D object detection, the ability to describe the fine-grained contours of any obstacles in the scene is the key insight of voxel occupancy representation, which facilitates subsequent tasks of autonomous driving. In this work, we propose CSV-Occ to address the following two challenges: (1) Existing methods fuse temporal information based on the attention mechanism, but are limited by high complexity. We extend the state space model to support multi-input sequence interaction and conduct temporal modeling in a cascaded architecture, thereby reducing the computational complexity from quadratic to linear. (2) Existing methods are limited by semantic ambiguity, resulting in the centers of foreground objects often being predicted as empty voxels. We enable the model to explicitly vote for the instance center to which the voxels belong and spontaneously learn to utilize the other voxel features of the same instance to update the semantics of the internal vacancies of the objects from coarse to fine. Experiments on the Occ3D-nuScenes dataset show that our method achieves state-of-the-art in camera-based 3D semantic occupancy prediction and also performs well on lidar point cloud semantic segmentation on the nuScenes dataset. Therefore, we believe that CSV-Occ is beneficial to the community and industry of autonomous vehicles.

Abstract Scene text image super‐resolution (STISR) has recently received considerable attention. Existing STISR methods are applicable to the situation that all the LR‐HR pairs are available. However, in real‐world scenarios, it is difficult and expensive to collect ground‐truth HR labels and align them with LR images, and thus it is essential to find a way to implement weakly supervised learning. We investigate the STISR problem in the situation that only a subset of HR labels is available and design a weak supervision framework using coarse‐grained text labels named TLWSR, which combines incomplete supervision and inexact supervision. Specifically, a lightweight text recognition network and connectionist temporal classification loss are used to guide the super‐resolution of text images during training. Extensive experiments on the benchmark TextZoom demonstrate that TLWSR generates distinguishable text images and exceeds the fully supervised baseline TSRN in boosting text recognition accuracywith only 50% HR labels available. Meanwhile, TLWSR can be applied to different super‐resolution backbones and significantly improves their performance. Furthermore, TLWSR shows good generalization capability to low‐quality images on scene text recognition benchmarks, which verifies the effectiveness of this framework. To the authors' knowledge, this is the first work exploring the problem of STISR in weakly supervised scenarios.

Matching all RGB photos of the target person in the gallery database with the full-body sketch image drawn by the professional is defined as Sketch re-identification (Sketch Re-id). The big gap between the sketch domain and RGB domain makes Sketch Re-id challenging. This paper addresses the problem by proposing a new framework to obtain domain-invariant features, which uses CNN as the backbone. To make the model focus more on the regions related to the sketch image in the RGB photo, we propose a novel cross-domain attention (CDA) mechanism. It uses different ways of splitting feature maps in its two branches and calculates the relationship between different parts in the sketch images and RGB photos. Moreover, we designed the cross-domain center loss (CDC), which breaks through the limitations that datasets need to be in the same domain in the traditional center loss. It effectively reduces the gap between two domains and makes the features with the same ID closer. The experiment is performed on the Sketch Re-id dataset. Each person has one sketch image and two RGB photos. To evaluate the generalization, we also experimented on two popular sketch-photo face datasets. The result in the Sketch Re-id dataset shows the model performs 3.7% higher than the previous methods. And the result in the CUHK student dataset performs 0.38% higher than the state-of-the-art methods.

At the end of 2019, a novel coronavirus COVID-19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID-19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi-class COVID-19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi-scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID-SemiSeg is also evaluated. The results demonstrate that this model outperforms other state-of-the-art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field.

Pulmonary nodules risk classification in adenocarcinoma is essential for early detection of lung cancer and clinical treatment decision. Improving the level of early diagnosis and the identification of small lung adenocarcinoma has been always an important topic for imaging studies. In this study, the authors propose a deep convolutional neural network (CNN) with scale‐transfer module (STM) and incorporate multi‐feature fusion operation, named STM‐Net. This network can amplify small targets and adapt to different resolution images. The evaluation data were obtained from the computed tomography (CT) database provided by Zhongshan Hospital Fudan University (ZSDB). All data have a pathological label and their lung adenocarcinomas risk are classified into four categories: atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma. The authors’ deep learning network STM‐Net was trained and tested for the risk stage prediction. The accuracy and the average area under the receiver operating characteristic curve achieved by their method are 95.455% and 0.987 for the ZSDB dataset. The experimental results show that STM‐Net largely boosts classification accuracy on the pulmonary nodules classification compared with state‐of‐the‐art approaches. The proposed method will be an effective auxiliary to help physicians diagnosis pulmonary nodules risk classification in adenocarcinoma in early‐stage.

The exponential increase in leakage power and the substantial power-saving opportunities provided by scheduling have made dual-threshold voltage (dual-Vth) an attractive choice for low-leakage-power designs. In this paper, we work under the assumption that functional units (FUs) are allocated after scheduling, and fully explore the solution space of scheduling with dual-Vthoperations to optimize the leakage power of the FUs. First, a binding conflict graph (BCG)-based scheduling method is presented to minimize the number of FUs. Second, the BCG-based method is extended to allow scheduling with dual-Vthoperation targeting the minimization of leakage power. In timing-constrained scheduling, each operation in the data flow is initialized with low-Vth. Then, starting from an operation schedule with the timing constraint satisfied, we scale the sets of low-Vthoperations in the off-critical paths with high-Vthso as to reduce the number of low-VthFUs without increasing the total delay. Finally, a scheduling method for minimizing the leakage power under both timing and resource constraints is presented. The results of benchmark tests show that the proposed algorithms can reduce the leakage power reported in previous works by 10.2% while maintaining high circuit performance.