Floquet Non-Abelian Topological Insulator and Multifold Bulk-Edge Correspondence

Topological phases characterized by non-Abelian charges are beyond the scope of the paradigmatic tenfold way and have gained increasing attention recently. Here we investigate topological insulators with multiple tangled gaps in Floquet settings and identify uncharted Floquet non-Abelian topological insulators without any static or Abelian analog. We demonstrate that the bulk-edge correspondence is multifold and follows the multiplication rule of the quaternion group $Q_8$. The same quaternion charge corresponds to several distinct edge-state configurations that are fully determined by phase-band singularities of the time evolution. In the anomalous non-Abelian phase, edge states appear in all bandgaps despite trivial quaternion charge. Furthermore, we uncover an exotic swap effect -- the emergence of interface modes with swapped driving, which is a signature of the non-Abelian dynamics and absent in Floquet Abelian systems. Our work, for the first time, presents Floquet topological insulators characterized by non-Abelian charges and opens up exciting possibilities for exploring the rich and uncharted territory of non-equilibrium topological phases.Comment: 8+7 page

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin

Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions

Semantic communication (SemCom) is an emerging technology that extracts useful meaning from data and sends only relevant semantic information. Thus, it has the great potential to improve the spectrum efficiency of conventional wireless systems with bit transmissions, especially in low signal-to-noise ratio (SNR) and small bandwidth regions. However, the existing works have mostly overlooked the constraints of mobile devices, which may not have sufficient capabilities to implement resource-demanding semantic encoder/decoder based on deep learning. To address this issue, we propose in this paper a new semantic relay (SemRelay), which is equipped with a semantic receiver to assist multiuser text transmissions. Specifically, the SemRelay decodes semantic information from a base station and forwards it to the users using conventional bit transmission, hence effectively improving text transmission efficiency. To study the multiuser resource allocation, we formulate an optimization problem to maximize the multiuser weighted sum-rate by jointly designing the SemRelay transmit power allocation and system bandwidth allocation. Although this problem is non-convex and hence challenging to solve, we propose an efficient algorithm to obtain its high-quality suboptimal solution by using the block coordinate descent method. Last, numerical results show the effectiveness of the proposed algorithm as well as superior performance of the proposed SemRelay over the conventional decode-and-forward (DF) relay, especially in small bandwidth region.Comment: 6 pages, 3 figures, accepted for IEEE Global Communication Conference (GLOBECOM) 2023 Workshop on Semantic Communication for 6

Instantaneous Rotational Speed Measurement Using Image Correlation and Periodicity Determination Algorithms

Dynamic and accurate measurement of instantaneous rotational speed is desirable in many industrial processes for both condition monitoring and safety control purposes. This paper presents a novel imaging based system for instantaneous rotational speed measurement. The low-cost imaging device focuses on the side surface of a rotating shaft without the use of a marker, entailing benefits of non-contact measurement, low maintenance and wide applicability. Meanwhile, new periodicity determination methods based on the Chirp-Z transform and parabolic interpolation based auto-correlation algorithm are proposed to process the signal of similarity level reconstructed using an image correlation algorithm. Experimental investigations are conducted on a purpose-built test rig to quantify the effects of the periodicity determination algorithm, frame rate, image resolution, exposure time, illumination conditions, and photographic angle on the accuracy and reliability of the measurement system. Experimental results under steady and transient operating conditions demonstrate that the system is capable of providing measurements of a constant or gradually varying speed with a relative error no greater than ±0.6% over a speed range from 100 to 3000 RPM (Revolutions Per Minute). Under step change conditions the proposed system can achieve valid speed measurement with a maximum error of 1.4%