HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding

The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate classification outcomes. However, the classification performance of HIID is constrained by the model's representational ability. To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a Feature Discrimination Module (FDM) is introduced to separate environment-related and category-related features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance. This novel approach holds promise for advancing the capabilities of hyperspectral image analysis by leveraging deep feature embedding principles. The implementation of the proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID for the sake of reproducibility.Comment: Submitted to IEEE TGR

A comprehensive thermo-viscoelastic experimental investigation of Ecoflex polymer

Silicone polymers have enormous applications, especially in the areas of biomedical engineering. Ecoflex, a commercially available room temperature cured silicone polymer, has attracted considerable attention due to its wide range of applications as medical-grade silicones and as matrix materials in producing nano-filled stretchable sensors and dielectric elastomers for soft robotics. In this contribution, we have conducted a wide range of experiments under thermo-mechanical loadings. These experiments consist of loading-unloading cyclic tests, single-step relaxation tests, Mullins effects tests at different strain rates and stretches, stress recovery tests at different rest time, etc. In order to assess the temperature influences on Ecoflex, a number of viscoelastic tests are performed in a thermal chamber with temperature ranging from -40°C to 140°C. Extensive experimental findings illustrate that Ecoflex experiences a significant stress softening in the first cycles and such a softening recovers gradually with respect to time. It also shows a significant amount of cyclic dissipations at various stretch levels as well as a considerable stress relaxation only for virgin samples. Cyclic dissipations and stress relaxation almost disappear for the case of pre-stretched samples. Furthermore, the material is more or less sensitive under a wide range of temperature differences

Superimposed RIS-phase Modulation for MIMO Communications: A Novel Paradigm of Information Transfer

Reconfigurable intelligent surface (RIS) is regarded as an important enabling technology for the sixth-generation (6G) network. Recently, modulating information in reflection patterns of RIS, referred to as reflection modulation (RM), has been proven in theory to have the potential of achieving higher transmission rate than existing passive beamforming (PBF) schemes of RIS. To fully unlock this potential of RM, we propose a novel superimposed RIS-phase modulation (SRPM) scheme for multiple-input multiple-output (MIMO) systems, where tunable phase offsets are superimposed onto predetermined RIS phases to bear extra information messages. The proposed SRPM establishes a universal framework for RM, which retrieves various existing RM-based schemes as special cases. Moreover, the advantages and applicability of the SRPM in practice is also validated in theory by analytical characterization of its performance in terms of average bit error rate (ABER) and ergodic capacity. To maximize the performance gain, we formulate a general precoding optimization at the base station (BS) for a single-stream case with uncorrelated channels and obtain the optimal SRPM design via the semidefinite relaxation (SDR) technique. Furthermore, to avoid extremely high complexity in maximum likelihood (ML) detection for the SRPM, we propose a sphere decoding (SD)-based layered detection method with near-ML performance and much lower complexity. Numerical results demonstrate the effectiveness of SRPM, precoding optimization, and detection design. It is verified that the proposed SRPM achieves a higher diversity order than that of existing RM-based schemes and outperforms PBF significantly especially when the transmitter is equipped with limited radio-frequency (RF) chains.Comment: Submitted to IEEE for possible publicatio

A Universal Framework of Superimposed RIS-Phase Modulation for MISO Communication

To fully exploit the additional dimension brought by reconfigurable intelligent surface (RIS), it is recently suggested by information theory that modulating information upon RIS phases is able to send extra information with increased communication rate. In this paper, we propose a novel superimposed RIS-phase modulation (SRPM) scheme to transfer extra messages by superimposing information-bearing phase offsets to conventionally optimized RIS phases. The proposed SRPM is interpreted as a universal framework for RIS phase modulation. Theoretical union bound of the average bit error rate (ABER) of the proposed SRPM is also derived with the maximum likelihood (ML) detection. The diversity order is characterized as 0.5 for all parameter settings, which is useful for determining the optimal choice of the phase modulation parameters. Furthermore, we discover that doubling the number of either RIS reflecting elements or the transmit antennas is equivalent to a 3 dB increment in the transmit power for SRPM. Numerical results demonstrate the effectiveness of SRPM and reveal that it achieves reliable communication of more bits than existing schemes.Comment: Accepted by IEEE Transactions on Vehicular Technolog

Imperfect CSI: A Key Factor of Uncertainty to Over-the-Air Federated Learning

Over-the-air computation (AirComp) has recently been identified as a prominent technique to enhance communication efficiency of wireless federated learning (FL). This letter investigates the impact of channel state information (CSI) uncertainty at the transmitter on an AirComp enabled FL (AirFL) system with the truncated channel inversion strategy. To characterize the performance of the AirFL system, the weight divergence with respect to the ideal aggregation is analytically derived to evaluate learning performance loss. We explicitly reveal that the weight divergence deteriorates as $\mathcal{O}(1/\rho^2)$ as the level of channel estimation accuracy $\rho$ vanishes, and also has a decay rate of $\mathcal{O}(1/K^2)$ with the increasing number of participating devices, $K$. Building upon our analytical results, we formulate the channel truncation threshold optimization problem to adapt to different $\rho$, which can be solved optimally. Numerical results verify the analytical results and show that a lower truncation threshold is preferred with more accurate CSI.Comment: Submitted to IEEE for possible publicatio

Influence of initial microcracks on the dynamic mechanical characteristics of sandstone

publishedVersio

Algorithms for Complex Systems Reliability Analysis Based on Bayesian Network

With the increase of complex systems functions, the number of its components will rise. This will lead to the amount of state combinations of components increasing exponentially. In order to solve this problem, a new compression algorithm and a new inference algorithm are developed to analyze the reliability of complex systems based on Bayesian Network in this paper. A satellite transmission system reliability is used to validate the proposed algorithms.This work was supported by the National Natural Science Foundation of China (No.51675525 and 11725211)