Fuhai Wang

This paper investigates the capabilities and effectiveness of backward localization centered on reconfigurable intelligent surfaces (RISs). In the backward sensing paradigm, the region of interest (RoI) is illuminated using a set of diverse radiation patterns. These patterns encode spatial information into a sequence of measurements, which are subsequently processed to reconstruct the RoI. We show that a single RIS can estimate the direction of arrival of incident waves by leveraging configurational diversity, and that the spatial diversity provided by multiple RISs further improves the accuracy of source localization and power estimation. The underlying structure of the sensing operator in the multi-snapshot measurement process is clarified. For single-RIS localization, the sensing operator is decomposed into a product of structured matrices, each corresponding to a specific physical process: wave propagation to and from the RIS, the relative phase offsets of elements with respect to the reference point, and the applied phase configuration of each element. A unified framework for identifying key performance indicators is established by analyzing the conditioning of the sensing operators. In the multi-RIS setting, we derive–via rank analysis–the governing law among the RoI size, the number of elements, and the number of measurements. Upper bounds on the relative error of the least squares reconstruction algorithm are derived. These bounds clarify how key performance indicators affect estimation error and provide valuable guidance for system-level optimization. Numerical experiments confirm that the trend of the relative error is consistent with the theoretical bounds. Finally, we develop a proof-of-concept prototype using universal software radio peripherals and employ a magnitude-only reconstruction algorithm tailored to the system. To the best of our knowledge, this represents the first experimental demonstration of its kind.

Accurate 3D localization is essential for realizing advanced sensing functionalities in next-generation Wi-Fi communication systems. This study investigates the potential of multistatic localization in Wi-Fi networks through the deployment of multiple cooperative antenna arrays. The collaborative gain offered by these arrays is twofold: (i) intra-array coherent gain at the wavelength scale among antenna elements, and (ii) inter-array cooperative gain across arrays. To evaluate the feasibility and performance of this approach, we develop WiCAL (Wi-Fi Collaborative Antenna Localization), a system built upon commercial Wi-Fi infrastructure equipped with uniform rectangular arrays (URAs). These arrays are driven by multiplexing embedded radio frequency (RF) chains available in standard access points or user devices, thereby eliminating the need for sophisticated, costly, and power-hungry multi-transceiver modules typically required in multiple-input and multiple-output (MIMO) systems. To address phase offsets introduced by RF chain multiplexing, we propose a three-stage, fine-grained phase alignment scheme to synchronize signals across antenna elements within each array. A bidirectional spatial smoothing MUSIC algorithm is employed to estimate angles of arrival (AoAs) and mitigate performance degradation caused by correlated interference. To further exploit inter-array cooperative gain, we elaborate on the synchronization mechanism among distributed URAs, which enables direct position determination by bypassing intermediate angle estimation. Once synchronized, the distributed URAs effectively form a virtual large-scale array, significantly enhancing spatial resolution and localization accuracy. WiCAL is validated using 3 × 4 URAs operating at the 5.2 GHz band. Experimental results demonstrate median AoA estimation errors of 1° in elevation and 1.5° in azimuth under intra-array coherent processing. For inter-array collaboration, the system achieves a median localization error of 15.6 cm using two URAs, outperforming state-of-the-art methods.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising auxiliary technology for radio frequency imaging. However, existing works face challenges of faint and intricate back-scattered waves and the restricted field-of-view (FoV), both resulting from complex target structures and a limited number of antennas. The synergistic benefits of multi-RIS-aided imaging hold promise for addressing these challenges. Here, we propose a dual-RIS-aided imaging system, Dreamer, which operates collaboratively in complementary modes (reflection-mode and transmission-mode). Dreamer significantly expands the FoV and enhances perception by deploying dual-RIS across various spatial and measurement patterns. Specifically, we perform a fine-grained analysis of how radio-frequency (RF) signals encode scene information in the scattered object modeling. Based on this modeling, we design illumination strategies to balance spatial resolution and observation scale, and implement a prototype system in a typical indoor environment. Moreover, we design a novel artificial neural network with a CNN-external-attention mechanism to translate RF signals into high-resolution images of human silhouettes. Our approach achieves an impressive structural similarity index (SSIM) score 0.83 surpassing state-of-the-art solutions, validating its effectiveness in broadening perception modes and enhancing imaging capabilities. The code to reproduce our results is available at https://github.com/fuhaiwang/Dreamer.

The reconfigurable intelligent surface (RIS) has garnered considerable attention due to its substantial potential in reconfiguring the electromagnetic environment. In RIS-aided communications, constrained ℓ ₂ -norm maximization problems frequently arise due to the phase configuration requirements. This paper investigates a general discrete ℓ _p -norm maximization problem, with power aggregation through RIS as a specific example. We propose a mathematically concise iterative framework composed of alternating inner product maximizations, which is well-suited for addressing both ℓ ₁ - and ℓ ₂ -norm maximizations under either discrete or continuous uni-modular variable constraints. The iteration process is proven to be monotonically non-decreasing. Additionally, this framework exhibits a distinctive capability to mitigate performance degradation caused by discrete quantization in practical systems, which is applicable to any algorithm intended for the continuous solution. Furthermore, as an integral component of the alternating iterations framework, we present a divide-and-sort (DaS) method to tackle the discrete inner product maximization problem. In the realm of ℓ _∞ -norm maximization, the DaS method ensures the identification of the global optimum with polynomial search complexity. We validate the proposed methods’ effectiveness and superiority through numerical and prototype experiments. Finally, we demonstrate that the proposed framework can be extended and applied to a wide range of other engineering problems.

Lorentz force particle analyzer (LFPA) is a non-contact electromagnetic measurement technique for the cleanliness inspection of liquid metal. Based on its principle, an optimized prototype of micro-particle measurement device is designed and built. Inclusion particles of different sizes in millimeter and submillimeter in liquid metal are inspected by this device at room temperature. The characteristics and formation mechanisms of typical inclusion particles pulse signal and laser path signal are compared and discussed. The wavelet decomposition method is used to denoise the original signal, and the desired signal is extracted and sorted out. Results show that LFPA is suitable for the measurement of inclusion particles in liquid metal.

Online microparticle detection is of utmost importance for industrial production. This paper proposes a signal processing and feature identification strategy to achieve particle size statistics for online measurement in a three-phase stirred tank reactor based on the electrical sensing zone (ESZ) method. Signal denoising and de-interference are achieved using the wavelet soft threshold method combined with mathematical morphological filtering. Pulse selection is implemented using pulse width limiting conditions. The key features that distinguish the pulse waveforms are defined based on the differences in the motion characteristics of the different types of particles through the aperture. Finally, the unsupervised classification algorithm balanced iterative reducing and clustering using hierarchies clustering is employed to distinguish the pulsed features between hard particles and bubbles. The results show that the particle size distribution identified by this strategy agrees with offline measurements indicating the effectiveness of the scheme. The effects of electromagnetic noise and the interference of small bubbles that approximate the particle size in solution in the online, in-situ measurement task are solved. This study scheme has a guiding and facilitating role in applying the ESZ principle to the industrial online measurement environment.

The T7 peptide, an active fragment of full‑length tumstatin [the non‑collagenous 1 domain of the type IV collagen α3 chain, α3 (IV) NC1], has exhibited potential antitumor effects in several types of cancer cells. However, the mechanism underlying its action against human hepatocellular carcinoma (HCC) remains unclear. The present study aimed to investigate the role of autophagy in T7 peptide‑induced cytotoxicity in HCC cells in vitro and in vivo. The results revealed that the T7 peptide significantly reduced cell viability and induced cell cycle arrest in HCC cells. The T7 peptide induced apoptosis in HCC cells through upregulation of Bax, Fas, and Fas ligand, and through upregulation of the anti‑apoptotic protein Bcl‑2. In addition, treatment with the T7 peptide induced protective autophagy in HCC cells. Blocking autophagy by 3‑methyladenineor bafilomycin A1 enhanced T7 peptide‑induced apoptosis. Furthermore, co‑treatment with MK‑2206 (an Akt specific inhibitor) or rapamycin (an inhibitor of mTOR) enhanced T7 peptide‑induced autophagy, whereas co‑treatment with insulin (an activator of the Akt/mTOR signaling pathway) alleviated T7 peptide‑induced autophagy, which suggested that the T7 peptide may induce autophagy activation via inhibition of the Akt/mTOR signaling pathway. Taken together, the present results demonstrated that suppression of autophagy potentiated the cytotoxic effects of the T7 peptide, and suggested that the T7 peptide may serve as a potential alternative compound for HCC therapy.

The P18 peptide is a functional fragment of pigment epithelial-derived factor (PEDF), which is an endogenic angiogenesis inhibitor. This study sought to determine the anti-angiogenic bioactivity of the P18 peptide in hepato-cellular carcinoma (HCC) and to elucidate the underlying mechanism. Xenograft tumour growth assays demonstrated the P18 peptide suppressed angiogenesis of HCC in vivo. Wound healing, Transwell and Matrigel-culture assays indicated that the P18 peptide inhibited the cell migration and tube formation of endothelial cells (ECs) in vitro. Cell viability and apoptosis assessed by Cell Counting Kit-8 (CCK-8) and flow cytometry assays suggested that the P18 peptide inhibited angiogenesis by inducing apoptosis of ECs. Angiogenesis- and signal transduction-associated molecules analysed by western blot demonstrated that the P18 peptide targets vascular endothelial cell growth factor receptor 2 (VEGFR2) on ECs. In conclusion, by inhibiting the phosphorylation of VEGFR2, the P18 peptide modulates signalling transduction between VEGF/VEGFR2 and suppresses activation of the PI3K/Akt cascades, leading to an increase in mitochondrial-mediated apoptosis and anti-angiogenic activity. This bioactivity of the P18 peptide may represent a novel therapeutic strategy for the treatment of HCC.

Let E be a real uniformly smooth Banach space and A : D(A) sube E rarr 2^E be a m-accretive mapping which satisfies a linear growth condition of the form ||u|| les C (1 + ||x||) for some constant C > 0 and for all x isin E and u isin Ax, z isin D(A) be an arbitrary element. Suppose A^-1 0 ne oslash. The sequence {x_n} sub D(A) is generated from arbitrary x₀ isin D (A) by x_n+l isin x_n -lambda_n (u_n + thetas_n (x_n - z)), u_n isin Ax_n, n ges 0, where {lambda_n} and {thetas_n{ are acceptably paired, then {x_n{ converges strongly to x* isin A^-(0). As its application, we have deduced a strong convergence theorem for the iterative algorithm of fixed points for continuous pseudocontractions.