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Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
In this article, we propose a novel complex convolutional neural network (CNN) CSI enhancer for integrated sensing and communications (ISAC), which exploits the correlation between the sensing parameters (such as angle-of-arrival and range) and the channel state information (CSI) to significantly improve the CSI estimation accuracy and further enhance the sensing accuracy.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities.
Section: Publications & Resources
Publication: IEEE Open Journal of Signal Processing
Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems.
Section: Publications & Resources
The deep learning-based profiling attacks have received significant attention for their potential against masking-protected devices. Currently, additional capabilities like exploiting only a segment of the side-channel traces or having knowledge of the specific countermeasure scheme have been granted to attackers during the profiling phase. In case either capability is removed, a practical profiling attack faces great difficulty and complexity.
Section: Publications & Resources
Aggregating metadata in the ciphertext field is an attractive property brought by homomorphic encryption (HE) for privacy-sensitive computing tasks, therefore, research on the next-generation wireless networks has treated it as one of the promising cryptographic techniques for various scenarios. However, existing schemes are far from being deployed in various computing scenarios due to their high computational complexity and ciphertext expansion, especially for bandwidth-limited and latency-sensitive wireless scenarios.
Section: Publications & Resources
Modern integrated circuits (ICs) require a complex, outsourced supply-chain, involving computer-aided design (CAD) tools, expert knowledge, and advanced foundries. This complexity has led to various security threats, such as Trojans inserted by adversaries during outsourcing, but also run-time threats like physical probing. Our proposed design-time solution, DEFense , is an extensible CAD framework for holistic assessment and proactive mitigation of multiple prominent threats.
Section: Publications & Resources
Recently, stacked intelligent metasurfaces (SIMs) have aroused widespread discussions as an innovative technology for directly processing electromagnetic (EM) wave signals. By stacking multiple programmable metasurface layers, an SIM has the ability to provide additional spatial degrees of freedom without the introduction of expensive radio-frequency chains, which may outperform reconfigurable intelligent surfaces (RISs) with single-layer structures.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
Image compression distortion can cause performance degradation of machine analysis tasks, therefore recent years have witnessed fast progress in developing deep image compression methods optimized for machine perception. However, the investigation still lacks for saliency segmentation. First, in this paper we propose a deep compression network increasing local signal fidelity of important image pixels for saliency segmentation, which is different from existing methods utilizing the analysis network loss for backward propagation.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
Pyramid Temporal Hierarchy Network (PTH-Net) is a new paradigm for dynamic facial expression recognition, applied directly to raw videos, without face detection and alignment. Unlike the traditional paradigm, which focus only on facial areas and often overlooks valuable information like body movements, PTH-Net preserves more critical information.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
Static meshes with texture maps have attracted considerable attention in both industrial manufacturing and academic research, leading to an urgent requirement for effective and robust objective quality evaluation. However, current model-based static mesh quality metrics (i.e., metrics that directly use the raw data of the static mesh to extract features and predict the quality) have obvious limitations: most of them only consider geometry information, while color information is ignored, and they have strict constraints for the meshes’ geometrical topology.
