UFPF: A Universal Feature Perception Framework for Microscopic Hyperspectral Images.

In recent years, deep learning has shown immense promise in advancing medical hyperspectral imaging diagnostics at the microscopic level. Despite this progress, most existing research models remain constrained to single-task or single-scene applications, lacking robust collaborative interpretation of microscopic hyperspectral features and spatial information, thereby failing to fully explore the clinical value of hyperspectral data. In this paper, we propose a microscopic hyperspectral universal feature perception framework (UFPF), which extracts high-quality spatial-spectral features of hyperspectral data, providing a robust feature foundation for downstream tasks.

SSF-Net: Spatial-Spectral Fusion Network With Spectral Angle Awareness for Hyperspectral Object Tracking.

Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously, making it highly suitable for handling challenges such as background clutter and visual similarity in object tracking. However, existing methods primarily focus on band regrouping and rely on RGB trackers for feature extraction, resulting in limited exploration of spectral information and difficulties in achieving complementary representations of object features. In this paper, a spatial-spectral fusion network with spectral angle awareness (SSF-Net) is proposed for hyperspectral (HS) object tracking.

BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking.

Hyperspectral object tracking (HOT) has many important applications, particularly in scenes where objects are camouflaged. The existing trackers can effectively retrieve objects via band regrouping because of the bias in the existing HOT datasets, where most objects tend to have distinguishing visual appearances rather than spectral characteristics. This bias allows a tracker to directly use the visual features obtained from the false-color images generated by hyperspectral images (HSIs) without extracting spectral features.

Generating Inverse Feature Space for Class Imbalance in Point Cloud Semantic Segmentation.

Point cloud semantic segmentation can enhance the understanding of the production environment and is a crucial component of vision tasks. The efficacy and generalization prowess of deep learning-based segmentation models are inherently contingent upon the quality and nature of the data employed in their training. However, it is often challenging to obtain data with inter-class balance, and training an intelligent segmentation network with the imbalanced data may cause cognitive bias.

SegHSI: Semantic Segmentation of Hyperspectral Images With Limited Labeled Pixels.

Hyperspectral images (HSIs), with hundreds of narrow spectral bands, are increasingly used for ground object classification in remote sensing. However, many HSI classification models operate pixel-by-pixel, limiting the utilization of spatial information and resulting in increased inference time for the whole image. This paper proposes SegHSI, an effective and efficient end-to-end HSI segmentation model, alongside a novel training strategy.

Robust Privacy-Preserving Recommendation Systems Driven by Multimodal Federated Learning.

Recommendation system (RS) is an important information filtering tool in nowadays digital era. With the growing concern on privacy, deploying RSs in a federated learning (FL) manner emerges as a promising solution, which can train a high-quality model on the premise that the server does not directly access sensitive user data. Nevertheless, some malicious clients can deduce user data by analyzing the uploaded model parameters.

Task-Wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images.

Arbitrary-oriented object detection (AOOD) has been widely applied to locate and classify objects with diverse orientations in remote sensing images. However, the inconsistent features for the localization and classification tasks in AOOD models may lead to ambiguity and low-quality object predictions, which constrains the detection performance. In this article, an AOOD method called task-wise sampling convolutions (TS-Conv) is proposed.

Central Attention Network for Hyperspectral Imagery Classification.

In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information. Based on the two principles, scaled dot-product central attention (SDPCA) tailored for HSI is designed to extract spectral-spatial information from a central pixel (i.e.

A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection.

Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts.

A new weighting scheme for arc based circle cone-beam CT reconstruction.

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich's helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ(x_,λ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x_.

Impacts of channel fluctuations on least-squares channel estimation in underwater acoustic communications.

The time-varying multipath introduces major distortions to transmissions in the underwater acoustic communication channel. Channel estimation is often used as one of the central steps to address such distortions in high-rate communication receivers. The focus of this paper is to quantify the impacts of the channel fluctuations on the performance of the least-squares channel estimator.

Exact and Robust Reconstructions of Integer Vectors Based on Multidimensional Chinese Remainder Theorem (MD-CRT).

The robust Chinese remainder theorem (CRT) has been recently proposed for robustly reconstructing a large nonnegative integer from erroneous remainders. It has found many applications in signal processing, including phase unwrapping and frequency estimation under sub-Nyquist sampling. Motivated by the applications in multidimensional (MD) signal processing, in this paper we propose the MD-CRT and robust MD-CRT for Integer vectors with respect to a general set of integer matrix moduli, which provides an algorithm to uniquely reconstruct integer vectors with respect to a general set of integer matrix moduli, which provides an algorithm to uniquely reconstruct an integer vector from its remainders, if it is in the fundamental parallelepiped of the lattice generated by a least common right multiple of all the moduli.

Beamforming Based Full-Duplex for Millimeter-Wave Communication.

In this paper, we study beamforming based full-duplex (FD) systems in millimeter-wave (mmWave) communications. A joint transmission and reception (Tx/Rx) beamforming problem is formulated to maximize the achievable rate by mitigating self-interference (SI). Since the optimal solution is difficult to find due to the non-convexity of the objective function, suboptimal schemes are proposed in this paper.

Road-Aided Ground Slowly Moving Target 2D Motion Estimation for Single-Channel Synthetic Aperture Radar.

To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method.

3D Geometry and Motion Estimations of Maneuvering Targets for Interferometric ISAR With Sparse Aperture.

In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA.

Range and Velocity Estimation of Moving Targets Using Multiple Stepped-frequency Pulse Trains.

Range and velocity estimation of moving targets using conventional steppedfrequencypulse radar may suffer from the range-Doppler coupling and the phasewrapping. To overcome these problems, this paper presents a new radar waveform namedmultiple stepped-frequency pulse trains and proposes a new algorithm. It is shown that byusing multiple stepped-frequency pulse trains and the robust phase unwrapping theorem(RPUT), both of the range-Doppler coupling and the phase wrapping can be robustlyresolved, and accordingly, the range and the velocity of a moving target can be accuratelyestimated.