Jamming Suppression Via Resource Hopping in High-Mobility OTFS-SCMA Systems

This letter studies the mechanism of uplink multiple access and jamming suppression in an OTFS system. Specifically, we propose a novel resource hopping mechanism for orthogonal time frequency space (OTFS) systems with delay or Doppler partitioned sparse code multiple access (SCMA) to mitigate the effect of jamming in controlled multiuser uplink. We analyze the non-uniform impact of classic jamming signals such as narrowband interference (NBI) and periodic impulse noise (PIN) in delay-Doppler (DD) domain on OTFS systems. Leveraging turbo equalization, our proposed hopping method demonstrates consistent BER performance improvement under jamming over conventional OTFS-SCMA systems compared to static resource allocation schemes

Reversible Non-Volatile Electronic Switching in a Near Room Temperature van der Waals Ferromagnet

The ability to reversibly toggle between two distinct states in a non-volatile method is important for information storage applications. Such devices have been realized for phase-change materials, which utilizes local heating methods to toggle between a crystalline and an amorphous state with distinct electrical properties. To expand such kind of switching between two topologically distinct phases requires non-volatile switching between two crystalline phases with distinct symmetries. Here we report the observation of reversible and non-volatile switching between two stable and closely-related crystal structures with remarkably distinct electronic structures in the near room temperature van der Waals ferromagnet Fe$_{5-\delta}$GeTe$_2$. From a combination of characterization techniques we show that the switching is enabled by the ordering and disordering of an Fe site vacancy that results in distinct crystalline symmetries of the two phases that can be controlled by a thermal annealing and quenching method. Furthermore, from symmetry analysis as well as first principle calculations, we provide understanding of the key distinction in the observed electronic structures of the two phases: topological nodal lines compatible with the preserved global inversion symmetry in the site-disordered phase, and flat bands resulting from quantum destructive interference on a bipartite crystaline lattice formed by the presence of the site order as well as the lifting of the topological degeneracy due to the broken inversion symmetry in the site-ordered phase. Our work not only reveals a rich variety of quantum phases emergent in the metallic van der Waals ferromagnets due to the presence of site ordering, but also demonstrates the potential of these highly tunable two-dimensional magnets for memory and spintronics applications

Multimedia Signal Processing based on Advanced Graph Approaches

When dealing with data residing on irregular and complex structures, graph signal processing(GSP) offers the ability to model and process them directly. While most existing GSP methods use graphs and edges to model pairwise relationships of such data, practical data may reside on more complex structures with multilateral interactions, which motivates processing methods with high-dimensional graphs like hypergraphs and multilayer graphs (MLG). Although both hypergraph signal processing (HGSP) and MLG have enjoyed additional notable successes in segmentation, clustering, and classification, it is not clear whether they can provide an advantage in specific applications such as point cloud resampling and motion segmentation. Also, it is shown that high-dimensional graph processing algorithms with high-dimensional graphs constructed for the whole data are computationally costly, especially when the number of nodes is large. Efficient processing and feature extraction of large-scale datasets are important in related computer vision and cyber-physical systems. In this dissertation, we investigate two applications for advanced graph approaches. The first application is point cloud resampling based on hypergraph signal processing to better explore the underlying relationship among different points in the point cloud and to extract contour-enhanced features. Specifically, we design hypergraph spectral filters to capture multi-lateral interactions among the signal nodes of point clouds and to better preserve their surface outlines. Without the need and the computation to first construct the underlying hypergraph, our low complexity approach directly estimates the hypergraph spectrum of point clouds by leveraging hypergraph stationary processes from the observed 3D coordinates. The second application is multilayer graph signal processing (M-GSP) based approaches to human motion segmentation. Specifically, our approach involves modeling spatial-temporal relationships of the human motion capture data via MLG and incorporating M-GSP spectrum analysis for feature extraction. Furthermore, this dissertation optimizes the computational complexity of key algorithms in iigraph based spectral analysis and processing methodologies. We propose an incremental EVD algorithm for low rank symmetric matrices (IEVD-LR) to update the top k eigen-pairs of the graph representation matrix. This algorithm has a novel error correction branch whenever the approximation error exceeds the defined tolerance. Equipped with the techniques presented in this dissertation, we extend the current research on advanced graph based processing to a variety of new directions. Based on our success in applying advanced graph-based processing algorithms to static point clouds and time-varying motion capture data, our exploration can extend to further applications of high-dimensional graph processing techniques on diverse multimedia datasets with varying structures, such as dynamic point clouds. We will also extend the low-complexity eigen-updating algorithm to general dynamic systems, where the number of data points may decrease over time. Another direction is to consider the singular space updating problem for the representation tensor of high-dimensional graphs, initialized by orthogonal CP decomposition or higher-order singular value decomposition result

Point Cloud Resampling Through Hypergraph Signal Processing

Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly motivate a plethora of research activities on large-scale point cloud processing and feature extraction. In this work, we investigate point cloud resampling based on hypergraph signal processing (HGSP). We develop a novel method to extract sharp object features and reduce the data size of point cloud representation. By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines. Experimental results validate the effectiveness of hypergraph in representing point clouds, and demonstrate the robustness of the proposed algorithm under noise.Comment: arXiv admin note: text overlap with arXiv:2103.0699

Rural Land Consolidation and Social Consciousness Change: A Case Study of a Land Consolidation Program in Rural Chongqing, China

With the changing relationship between urban and rural areas in China, the rural areas are experiencing rapid social transformation. To ensure successful implementation of the rural revitalization strategy, land consolidation has become a major measure of rural economic reform. Existing research focuses on quantitative studies exploring the relationship between land consolidation and rural economic development, but there is a lack of studies on the relationship between land consolidation and social change. In this study, we utilized Rocha’s conceptual framework for community empowerment and selected Jin’an Village as our study area, using semi-structured interviews and semi-participatory observation to obtain original materials, with the aim of providing a detailed description of the specific practice of land consolidation and analyzing the impact of land consolidation on the transformation of rural social consciousness. The study found that the participatory practices of Chinese rural villagers in the land consolidation process are consistent with the development process of community empowerment. Rural land consolidation involves villagers in the land consolidation process, which can effectively stimulate villagers’ participation in public affairs. Concurrently, the interaction between villagers and outside investors disrupts the conventional socialization model in rural areas and motivates villagers to act in accordance with contractual agreements. The conclusion is that land consolidation in rural areas can enhance the political democracy and legal consciousness of local villagers, which can lead to a change in local social consciousness. Our findings also emphasize the crucial necessity of providing rural villagers with improved accessibility to professional services and information, coupled with the continued promotion of land consolidation to advance modernization in these areas

Graph Signal Processing over Multilayer Networks -- Part I: Foundations and Spectrum Analysis

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life datasets and systems are characterized by more complex interactions among distinct entities, which may represent multi-level interactions that are difficult to be modeled with a single-layer graph, and can instead be captured by multiple layers of graph connections. Such multilayer/multi-level data structure can be modeled more naturally using a high-dimensional multilayer network (MLN). This work generalizes traditional graph signal processing (GSP) over multilayer networks for the analysis of multi-level signal features and their interactions. We propose a tensor-based framework of multilayer network signal processing (M-GSP) in this two-part series. Specifically, Part I introduces the fundamentals of M-GSP and studies spectrum properties of MLN Fourier space. We further describe its connections to traditional digital signal processing and GSP. Part II focuses on the major tools within the M-GSP framework for signal processing and data analysis. We provide results to demonstrate the efficacy and benefits of applying multilayer networks and the M-GSP in practical scenarios