Modulation Spaces Research Articles

Weamba: Weather-Degraded Remote Sensing Image Restoration with Multi-Router State Space Model

Adverse weather conditions, such as haze and raindrop, consistently degrade the quality of remote sensing images and affect subsequent vision-based applications. Recent years have witnessed advancements in convolutional neural networks (CNNs) and Transformers in the field of remote sensing image restoration. However, these methods either suffer from limited receptive fields or incur quadratic computational overhead, leading to an imbalance between performance and model efficiency. In this paper, we propose an effective vision state space model (called Weamba) for remote sensing image restoration by modeling long-range pixel dependencies with linear complexity. Specifically, we develop a local-enhanced state space module to better aggregate rich local and global information, both of which are complementary and beneficial for high-quality image reconstruction. Furthermore, we design a multi-router scanning strategy for spatially varying feature extraction, alleviating the issue of redundant information caused by repeated scanning directions in existing methods. Extensive experiments on multiple benchmarks show that the proposed Weamba performs favorably against state-of-the-art approaches.

Low-light image enhancement via multi-stream vision state space module

Low-light image enhancement via multi-stream vision state space module

UAV selection for high-speed train communication using OTFS modulation

Providing continuous wireless connectivity for high-speed trains (HSTs) is challenging due to their high speeds, making installing numerous ground base stations (BSs) along the HST route an expensive solution, particularly in rural and wilderness areas. This paper proposes using multiple unmanned aerial vehicles (UAVs) to deliver high data rate wireless connectivity for HSTs, taking advantage of their ability to fly, hover, and maneuver at low altitudes. However, autonomously selecting the optimal UAV by the HST is challenging. The chosen UAV should maximize the HST’s achievable data rate and provide an extended HST coverage period to minimize frequent UAV handovers constrained by the UAV’s limited battery capacity. The optimization challenge arises from accurately estimating each UAV’s expected coverage period for the HST, given both are moving at high speeds and the UAV’s flying altitude is unknown to the HST. This paper utilizes the estimated HST-UAV channel parameters in the delay-doppler (DD) domain, employing orthogonal time frequency space (OTFS) modulation, to estimate the relative speeds between the HST and UAVs, as well as the UAVs’ flying altitudes. Based on these estimates, HST can predict the maximum coverage period each UAV provides, allowing for selecting the best UAV while considering their remaining battery capacities. Numerical analysis demonstrates the effectiveness of the proposed approach compared to other benchmarks in various scenarios.

Iterative Maximum Ratio Combining Detector for Satellite Multiple-Input Multiple-Output/Orthogonal Time–Frequency Space Systems Based on Soft-Symbol Interference Cancelation

Orthogonal time–frequency space (OTFS) modulation combined with massive multiple-input multiple-output (MIMO) can simultaneously address the problems caused by multipath delay, the Doppler effect, and channel fading. To mitigate inter-subcarrier and inter-symbol interference in satellite–terrestrial MIMO-OTFS systems, an iterative maximum ratio combining detection algorithm based on hard-decision interference cancelation (ICH-IMRC) is proposed. The signal detection is iterated by performing MRC on the interference-canceled received symbols. To mitigate the error spread in the interference cancelation process, iterative maximum ratio combining detection based on soft symbol interference cancelation (S-IMRC) is proposed, which is improved based on ICH-IMRC. The interference cancelation is updated by the expectation of other symbols, and the expectation and variance of symbols are updated by soft judgment with the posterior probability of symbols. To improve the detection convergence speed, optimal relaxation parameters are obtained based on the Sparrow Search Algorithm (SSA). Simulation results show that the proposed S-IMRC has superior error rate performance compared to the conventional algorithms for satellite–terrestrial MIMO-OTFS systems. Furthermore, the proposed algorithm is applicable to various satellite channel models and achieves excellent BER for different orders of orthogonal amplitude-modulated signals and different antenna array sizes.

On a Class of Nonlinear Plate Equations on Modulation Spaces

On a Class of Nonlinear Plate Equations on Modulation Spaces

Conduction band photonic trapping via band gap reversal of brookite quantum dots using controlled graphitization for tuning a multi-exciton photoswitchable high-performance semiconductor.

Brookite exists as the metastable phase of titania and often mediates the transformation of anatase to rutile. The photocatalytic competence of brookite relative to polymorphs anatase and rutile has generally been considered structurally and energetically unfavourable for reasons that remain largely unknown and unchallenged. However, the process of phase transformation and performance related cooperativity among all three polymorphs has recently unlocked alternative directions for exploring brookite photovoltaics. Here, we demonstrate the programmable re-configuration of anatase to quantum confined reduced graphene (rGO)-brookite and show it is entirely modulated by surface-driven effects. Key components to this mechanism suggest that the self-assembly of rGO-brookite quantum dots is defect driven through pathways that favour a direct-to-indirect band gap reversal resulting from the graphitization of brookite. The accompaniment of new bandgap characteristics under quantum confinement introduce new hybridized energy states at the graphitic carbon-brookite juncture by modulation of the intrinsic sp2 character to extrinsic sp3 clusters intermediate to graphene quantum dots (GQDs) and graphene oxide quantum dots (GOQDs). Evidenced by the intercalation of photochromic/fluorescent carbazole and anthracene moieties within the rGO framework by self-assembly, we show that the acquired fluorescence and luminescence properties of rGO-brookite are multi-emissive and reversibly quenchable under light excitation and from solvent polarity differences. Further, tuning the excitonic response of rGO-brookite by modulation of the photoluminescence (PL) signal intensity signifies coordinated interaction between localised carbazole and benz(a)anthracene moities which can undergo further structural refinement to adapt more optimally to both internal and external energy waves. Distinguishable by a large red-shift in the photoluminescent emission peak at λ479 nm in the NIR region, we infer that a photoelectron sink driven by the quantum confinement of a narrow band gap of 0.78 eV formed from the orbital overlap of unoccupied interfacial sites promotes strong e-h+ coupling in the hybridized defect structure imposing a high charge separation by hindering e-h+ recombination. Modulation of interlayer spacing between rGO sheets and the synergy of complexation between intercalated carbazole/benz(a)nthracene can be adapted to achieve rapid photodegradation characteristics for DSSC applications.

On some properties of modulation spaces as Banach algebras

On some properties of modulation spaces as Banach algebras

Deep Learning-Based Detection for RSMA with Orthogonal Time Frequency Space Modulation

Deep Learning-Based Detection for RSMA with Orthogonal Time Frequency Space Modulation

Structure of blood cell-specific tubulin and demonstration of dimer spacing compaction in a single protofilament.

Microtubule (MT) function plasticity originates from its composition of α- and β-tubulin isotypes and the post-translational modifications of both subunits. Aspects such as MT assembly dynamics, structure, and anticancer drug binding can be modulated by αβ-tubulin heterogeneity. However, the exact molecular mechanism regulating these aspects is only partially understood. A recent insight is the discovery of expansion and compaction of the MT lattice, which can occur via fine modulation of dimer longitudinal spacing mediated by GTP hydrolysis, taxol binding, protein binding, or isotype composition. Here, we report the first structure of the blood cell-specific α1/β1-tubulin isolated from the marginal band of chicken erythrocytes (ChET) determined to a resolution of 3.2 Å by cryo-EM. We show that ChET rings induced with Cryptophycin-52 (Cp-52) are smaller in diameter than HeLa tubulin (HeLaT) rings induced with Cp-52 and composed of the same number of heterodimers. We observe compacted interdimer and intradimer curved protofilament interfaces, characterized by shorter distances between ChET subunits and accompanied by conformational changes in the β-tubulin subunit. The compacted ChET interdimer interface brings more residues near the Cp-52 binding site. We measured the Cp-52 apparent binding affinities of ChET and HeLaT by mass photometry, observing small differences, and detected the intermediates of the ring assembly reaction. These findings demonstrate that compaction/expansion of dimer spacing can occur in a single protofilament context and that the subtle structural differences between tubulin isotypes can modulate tubulin small molecule binding.

Modulation of Ti3C2Tx interlayer spacing and functional groups by Lewis‐basic halides and their effects on Li+ storage properties

AbstractSurface and interfacial chemistry play a vital role in shaping the properties of two‐dimensional transition metal carbides and nitrides (MXenes). This study focuses on utilizing Lewis‐basic halides (LiCl/KCl) for thermal treatment of multilayered Ti3C2Tx, leading to the simultaneous modulation of interlayer spacing and surface functional groups. Compared to the pristine Ti3C2Tx, the LiCl/KCl treated sample (heating temperature: 450°C, denoted as LK‐Ti3C2Tx‐450) showcases a remarkable increase in the interlayer spacing and synergistic optimization of the functional groups. These modifications endow LK‐Ti3C2Tx‐450 with enhanced electrochemical properties, rendering it as a promising anode candidate for lithium‐ion batteries. The increased interlayer spacing is particularly advantageous, as it facilitates efficient and rapid Li+ diffusion, a vital factor in enhancing the performance of energy storage devices.

Modulation of the Atomic Spacing of Electrocatalytic for Boosting Reactive Oxygen Species Production to Precise Hepatocellular Carcinoma Cell Apoptosis.

Promoting tumor cell apoptosis through the catalytic regulation of reactive oxygen species (ROS) is an ideal therapeutic option for cancer. However, a stable and controllable exogenous source of ROS is still lacking. Efficient and controllable electrocatalysis has shown tremendous potential for cancer treatment, but its key challenge lies in achieving precise, efficient, and controllable electrocatalytic ROS production at the tumor site. This study describes an electrocatalytic treatment technique for hepatocellular carcinoma (HCC) based on traditional Chinese acupuncture. By attaching a biocompatible electrocatalyst NiO-P700 with optimal atomic spacing to the surface of silver acupuncture needles, a high-concentration ROS microenvironment was generated around tumor cells via ORRs when the needles were electrified. This induction led to the accumulation of inflammatory factors (IL-1β, IL-6, and TNF-α) and macrophage infiltration, accelerating tumor cell apoptosis and necrosis. Both in vitro and in vivo experiments demonstrated that the rate of ROS production can be rapidly controlled by adjusting voltage and current. Importantly, the high concentration of ROS can be safely and effectively confined to the lesion site without affecting the entire body. Our study attempted to integrate electrocatalysis and acupuncture in HCC treatment, successfully regulating NiO-P atomic spacing and enhancing ORR performance, thereby presenting a safe and reliable perspective for HCC therapy.

Emergence of Exotic Spin Texture in Supramolecular Metal Complexes on a 2D Superconductor.

Designer heterostructures have offered a very powerful strategy to create exotic superconducting states by combining magnetism and superconductivity. In this Letter, we use a heterostructure platform combining supramolecular metal complexes (SMCs) with a quasi-2D van der Waals superconductor NbSe_{2}. Our scanning tunneling microscopy measurements demonstrate the emergence of Yu-Shiba-Rusinov bands arising from the interaction between the SMC magnetism and the NbSe_{2} superconductivity. Using x-ray absorption spectroscopy and x-ray magnetic circular dichroism measurements, we show the presence of antiferromagnetic coupling between the SMC units. These result in the emergence of an unconventional 3×3 reconstruction in the magnetic ground state that is directly reflected in real space modulation of the Yu-Shiba-Rusinov bands. The combination of flexible molecular building blocks, frustrated magnetic textures, and superconductivity in heterostructures establishes a fertile starting point to fabricating tunable quantum materials, including unconventional superconductors and quantum spin liquids.

Piecewise Linear and Step Fourier Multipliers for Modulation Spaces

Piecewise Linear and Step Fourier Multipliers for Modulation Spaces

Symbiotic Radio With Orthogonal Time Frequency Space Modulation Over High-Mobility Channels

Symbiotic Radio With Orthogonal Time Frequency Space Modulation Over High-Mobility Channels

Rigid body modular modeling method for multiple reusable component weapon systems based on bond space theory

Abstract This article proposes a rigid body modular modeling method based on bond space theory. Aiming at the rigid components with the same characteristics that are heavily used in artillery weapon systems, the concept of bond space sub-model is proposed, and the rigid body module and constraint module are established and deduced. Considering the differences between ideal constraints and actual constraints such as gaps or heat losses, an elastic constraint bonding space module is proposed to improve the representation of ideal constraints. By combining bond space modules with the same characteristics and solving them, the process of analyzing complex systems will be greatly simplified. In addition, a study case of ammunition box feeding is presented to investigate the computational results of this modeling method; the analysis results show that this modeling method has good computational accuracy on the basis of improved computational efficiency.

On Dirac equations with Hartree type nonlinearity in modulation spaces

On Dirac equations with Hartree type nonlinearity in modulation spaces

Multi-Pilot Channel Estimation for Orthogonal Time-Frequency Space Systems Based on Constant-Amplitude Zero-Autocorrelation Sequences.

Future communication systems must support high-speed mobile scenarios, while the mainstream Orthogonal Frequency Division Multiplexing (OFDM) technology faces severe inter-carrier interference in such environments. Therefore, the adoption of Orthogonal Time-Frequency Space (OTFS) modulation in 6G systems is an effective solution. The widely used single-pilot channel estimation in OTFS systems is susceptible to path loss and inaccurate fading coefficient estimation, leading to reduced estimation accuracy, signal distortion, and degraded overall system communication quality. To address this problem, this paper proposes a Constant-Amplitude Zero-Autocorrelation (CAZAC) sequence-based multi-pilot OTFS channel estimation scheme. The proposed method inserts multiple low-power pilots in the delayed Doppler domain (DD) and employs joint signal processing at the receiver to effectively suppress noise, thereby significantly improving the accuracy and reliability of channel estimation. Additionally, this paper analyzes the impact of CAZAC sequence length on estimation performance and provides reasonable parameter selection recommendations. In summary, this work proposes an innovative solution to the channel estimation challenge in OTFS systems, laying a solid theoretical foundation for the realization of future high-speed mobile communication technologies such as 6G, with important academic value and application prospects.

Research on an Algorithm for High-Speed Train Positioning and Speed Measurement Based on Orthogonal Time Frequency Space Modulation and Integrated Sensing and Communication

The Doppler effect caused by the rapid movement of high-speed rail services has a great impact on the accuracy of train positioning and speed measurement. Existing train positioning algorithms require a large number of trackside equipment and sensors, resulting in high construction and maintenance costs. Aiming to solve the above two problems, this article proposes a train positioning algorithm based on orthogonal time–frequency space (OTFS) modulation and integrated sensing and communication (ISAC). Firstly, based on the OTFS, the positioning and speed measurement architecture of communication awareness integration is constructed. Secondly, a two-stage estimation (TSE) algorithm is proposed to estimate the delay Doppler parameters of HST. In the first stage, a low-complexity coarse grid search is used, and in the second stage, a refined off-grid search is used to obtain the delay Doppler parameters. Then, the time difference of arrival/frequency difference of arrival (TDOA/FDOA) algorithm based on multiple base stations is used to locate the target, the weighted least square method is used to calculate the location, and the Cramér–Rao lower bound (CRLB) for positioning and speed measurement is derived. The simulation results demonstrate that, compared to GNSS/INS and OFDM radars, the algorithm exhibits enhanced positioning and speed measurement accuracy.

Signal detection of M-MIMO-orthogonal time frequency space modulation using hybrid algorithms: ZFE + MMSE and ZFE + MF

Orthogonal Time Frequency Space (OTFS) modulation, coupled with Massive Multiple Input Multiple Output (Massive-MIMO) technology, presents a promising avenue for enhancing the efficiency and reliability of fifth-generation (5G) and beyond fifth-generation (B5G) systems. OTFS modulation offers robust communication in high-mobility environments by converting signals into the delay-Doppler domain, ensuring better performance over fading channels. MIMO enhances wireless networks by using large antenna arrays to boost capacity, spectral efficiency, and reliability, making both technologies vital for next-generation radio systems. In this study, we explore the detection of (8 × 8, 16 × 16, 64 × 64, and 256 × 256) Massive-MIMO-OTFS signals utilizing two prominent detection algorithms: zero forcing equalization (ZFE) with matched filter (MF) known as (ZFE + MF) and Zero Forcing with minimum mean square error (MMSE) known as (ZFE + MMSE). The combination of Massive MIMO and OTFS offers improved spectral efficiency, robustness against fading, and enhanced spatial multiplexing capabilities. The parameters such as bit error rate (BER) and power spectral density (PSD) are analyzed and estimated for the proposed hybrid and conventional algorithms. The proposed algorithms obtained the SNR and PSD gain of 3.2 dB, 3.2 dB, 4.8 dB, and 6.1 dB gain, respectively, for 8 × 8, 16 × 16, 64 × 54, and 256 × 256 MIMO systems. Further, the PSD gain of -390 is obtained for the 256 × 256 system, resulting in high spectral efficiency.

VMC‐UNet: A Vision Mamba‐CNN U‐Net for Tumor Segmentation in Breast Ultrasound Image

ABSTRACTBreast cancer remains one of the most significant health threats to women, making precise segmentation of target tumors critical for early clinical intervention and postoperative monitoring. While numerous convolutional neural networks (CNNs) and vision transformers have been developed to segment breast tumors from ultrasound images, both architectures encounter difficulties in effectively modeling long‐range dependencies, which are essential for accurate segmentation. Drawing inspiration from the Mamba architecture, we introduce the Vision Mamba‐CNN U‐Net (VMC‐UNet) for breast tumor segmentation. This innovative hybrid framework merges the long‐range dependency modeling capabilities of Mamba with the detailed local representation power of CNNs. A key feature of our approach is the implementation of a residual connection method within the U‐Net architecture, utilizing the visual state space (VSS) module to extract long‐range dependency features from convolutional feature maps effectively. Additionally, to better integrate texture and structural features, we have designed a bilinear multi‐scale attention module (BMSA), which significantly enhances the network's ability to capture and utilize intricate feature details across multiple scales. Extensive experiments conducted on three public datasets demonstrate that the proposed VMC‐UNet surpasses other state‐of‐the‐art methods in breast tumor segmentation, achieving Dice coefficients of 81.52% for BUSI, 88.00% for BUS, and 88.96% for STU. The source code is accessible at https://github.com/windywindyw/VMC‐UNet.