Search Results (1,243)

Impact monitoring is an effective approach to ensuring the safety of composite structures. The accuracy of current algorithms mostly depends on the number of physical sensors, which is not an economical way for large-area composite structures. In order to combine the advantages of sparse and dense arrays, a two-stage collaborative approach is proposed to locate the general areas and precise positions of impacts on composite structures. In Stage I, the steering vector information of the possible position is simulated according to the principle of array sensor signal processing, and a virtual array sparse feature map is constructed. When an actual impact arrives, a similarity algorithm is then used to find the suspected area in the map, which narrows down the search area to a large extent. In Stage II, a compensated two-dimensional multiple signal classification (2D-MUSIC) algorithm-based imaging method is applied to estimate the precise position of the impact in the suspected area. Finally, the accuracy and effectiveness of the proposed method are validated by numerical simulation and experiments on a carbon fiber composite panel. Both numerical and experimental results verify that the two-stage impact location method can effectively monitor composite structures with sufficient accuracy and efficiency. Full article

Sub-array-level digital arrays effectively diminish the computational complexity and sample demand of space-time adaptive processing (STAP), thus finding extensive applications in many airborne platforms. Nonetheless, airborne sub-array-level digital array radar still encounters pronounced performance deterioration in highly heterogeneous clutter environments due to inadequate training samples. To address this issue, a clutter-sensing-driven STAP approach for airborne sub-array-level digital arrays is proposed in this paper. Firstly, we derive a signal model of sub-array-level clutter sensing in detail and then further analyze the influence of the sidelobe characteristics of the conventional sub-array joint beam on clutter sensing. Secondly, a sub-array joint beam optimization model is proposed, which optimizes the sub-array joint beam into a wide beam with flat-top characteristics to improve the clutter-sensing performance in the beam sidelobe region. Finally, we decompose the complex optimization problem into two subproblems and then relax them into the low sidelobe-shaped beam pattern synthesisproblem and second-order cone programming problem, which can be effectively solved. The effectiveness of the proposed approach is validated in a real clutter environment through numerical experiments. Full article

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure. Full article

High Mobility Group Box 1 (HMGB1) is a highly conserved non-histone chromatin-associated protein across species, primarily recognized for its regulatory impact on vital cellular processes, like autophagy, cell survival, and apoptosis. HMGB1 exhibits dual functionality based on its localization: both as a non-histone protein in the nucleus and as an inducer of inflammatory cytokines upon extracellular release. Pathophysiological insights reveal that HMGB1 plays a significant role in the onset and progression of a vast array of diseases, viz., atherosclerosis, kidney damage, cancer, and neurodegeneration. However, a clear mechanistic understanding of HMGB1 release, translocation, and associated signaling cascades in mediating such physiological dysfunctions remains obscure. This review presents a detailed outline of HMGB1 structure–function relationship and its regulatory role in disease onset and progression from a signaling perspective. This review also presents an insight into the status of HMGB1 druggability, potential limitations in understanding HMGB1 pathophysiology, and future perspective of studies that can be undertaken to address the existing scientific gap. Based on existing paradigm of various studies, HMGB1 is a critical regulator of inflammatory cascades and drives the onset and progression of a broad spectrum of dysfunctions. Studies focusing on HMGB1 druggability have enabled the development of biologics with potential clinical benefits. However, deeper understanding of post-translational modifications, redox states, translocation mechanisms, and mitochondrial interactions can potentially enable the development of better courses of therapy against HMGB1-mediated physiological dysfunctions. Full article

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops two accelerators, as the core of the signal measurement system, for intelligent signal processing. Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. Subsequently, another accelerator is designed with the efficient hardware structure of the long short-term memory (LSTM) + DNN model, demonstrating outstanding performance with a classification accuracy of 98.82%, mean absolute error (MAE) of 0.27°, and root mean square errors (RMSE) of 0.392° after model compression. Moreover, parallel optimization strategies are exploited to further reduce latency and support simultaneous frequency and direction measurement tasks. Finally, we test the actual collected signal data on the XCVU13P field programmable gate array (FPGA). The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). In addition, the parallel strategies further decrease the delay by 23.9% and 37.5% when processing continuous data. The FPGA-based and deep learning-assisted hardware accelerators significantly improve real-time performance and provide a promising solution for signal measurement. Full article

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process and the calculated data obtained by modeling mathematical models of the digital twin system of the cutting process. This approach is justified by the fact that some coordinates for the state of the cutting process cannot be measured, and the vibration signals measured by the vibration monitoring system (the vibration acceleration of the tip of the cutting tool) are subject to external disturbing influences. Both the experimental method and the Matlab 2022b simulation method were used as research methods. The experimental research method is based on the widespread use of modern analog vibration transducers, the signals from which undergo the process of digitalization and further processing in order to identify arrays of additional information required for virtual digital twin models. The results obtained allow us to formulate a new conceptual approach to the construction of systems for determining the degree of cutting tool wear, based on the joint use of computational virtual models of the digital twin system and data obtained from the vibration monitoring system of the cutting process. Full article

For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees of freedom and low mutual coupling, a height measurement method based on a sparse array is proposed. First, a practical signal model of MIMO radar based on a sparse array is established. Then, the modified multiple signal classification (MUSIC) and maximum likelihood (ML) estimation algorithms based on two classical sparse arrays (coprime array and nested array) are proposed. To reduce the complexity of the algorithm, a real-valued processing algorithm for generalized MUSIC (GMUSIC) and maximum likelihood is proposed, and a reduced dimension matrix is introduced into the real-valued processing algorithm to further reduce computation complexity. Finally, sufficient simulation results are provided to illustrate the effectiveness and superiority of the proposed technique. The simulation results show that the height measurement accuracy can be efficiently improved by using our proposed technique for both simple and complex terrain. Full article

With the rapid growth of connected vehicles and the vulnerability of embedded systems against cyber attacks in an era where quantum computers are becoming a reality, post-quantum cryptography (PQC) is a crucial solution. Yet, by nature, automotive sensors are limited in power, processing capability, memory in implementing secure measures. This study presents a pioneering approach to securing automotive systems against post-quantum threats by integrating the Ascon cipher suite—a lightweight cryptographic protocol—into embedded automotive environments. By combining Ascon with the Controller Area Network (CAN) protocol on an Artix-7 Field Programmable Gate Array (FPGA), we achieve low power consumption while ensuring high performance in post-quantum-resistant cryptographic tasks. The Ascon module is designed to optimize computational efficiency through bitwise Boolean operations and logic gates, avoiding resource-intensive look-up tables and achieving superior processing speed. Our hardware design delivers significant speed improvements of 100 times over software implementations and operates effectively within a 100 MHz clock while demonstrating low resource usage. Furthermore, a custom digital signal processing block supports CAN protocol integration, handling message alignment and synchronization to maintain signal integrity under automotive environmental noise. Our work provides a power-efficient, robust cryptographic solution that prepares automotive systems for quantum-era security challenges, emphasizing lightweight cryptography’s readiness for real-world deployment in automotive industries. Full article

Radar imaging is a technology that uses radar systems to generate target images. It transmits radio waves, receives the signal reflected back by the target, and realizes imaging by analyzing the target’s position, shape, and motion information. The three-dimensional (3D) forward-looking imaging of missile-borne radar is a branch of radar imaging. However, owing to the limitation of antenna aperture, the imaging resolution of real aperture radar is restricted. By implementing the super-resolution techniques in array signal processing into missile-borne radar 3D forward-looking imaging, the resolution can be further improved. In this paper, a 3D forward-looking imaging algorithm based on the two-dimensional (2D) super-resolution algorithm is proposed for missile-borne planar array radars. In the proposed algorithm, a forward-looking planar array with scanning beams is considered, and each range-pulse cell in the received data is processed one by one using a 2D super-resolution method with the error function constructed according to the weighted least squares (WLS) criterion to generate a group of 2D spectra in the azimuth-pitch domain. Considering the lack of training samples, the super-resolution spectrum of each range-pulse cell is estimated via adaptive iteration processing only with one sample, i.e., the cell under process. After that, all the 2D super-resolution spectra in azimuth-pitch are accumulated according to the changes in instantaneous beam centers of the beam scanning. As is verified by simulation results, the proposed algorithm outperforms the real aperture imaging method in terms of azimuth-pitch resolution and can obtain 3D forward-looking images that are of a higher quality. Full article

This paper presents a programmable digital filtering unit dedicated to operating with signals from infrared (IR) detection modules. The designed device is quite useful for increasing the signal-to-noise ratio due to the reduction in noise and interference from detector–amplifier circuits or external radiation sources. Moreover, the developed device is flexible due to the possibility of programming the desired filter types and their responses. In the circuit, an advanced field-programmable gate array FPGA chip was used to ensure an adequate number of resources that are necessary to implement an effective filtration process. The proposed circuity was assisted by a 32-bit microcontroller to perform controlling functions and could operate at frequency sampling of up to 40 MSa/s with 16-bit resolution. In addition, in our application, the sampling frequency decimation enabled obtaining relatively narrow passband characteristics also in the low frequency range. The filtered signal was available in real time at the digital-to-analog converter output. In the paper, we showed results of simulations and real measurements of filters implementation in the FPGA device. Moreover, we also presented a practical application of the proposed circuit in cooperation with an InAsSb mid-IR detector module, where its self-noise was effectively reduced. The presented device can be regarded as an attractive alternative to the lock-in technique, artificial intelligence algorithms, or wavelet transform in applications where their use is impossible or problematic. Comparing the presented device with the previous proposal, a higher signal-to-noise ratio improvement and wider bandwidth of operation were obtained. Full article

To tackle the issue of poor accuracy in single-snapshot data processing for Direction of Arrival (DOA) estimation in passive radar systems, this paper introduces a method for judiciously leveraging multi-snapshot data. This approach effectively enhances the accuracy of DOA estimation and spatial angle resolution in passive radar systems. Additionally, in response to the non-convex nature of the mixed norm, we propose a hyperbolic tangent model as a replacement, transforming the problem into a directly solvable convex optimization problem. The rationality of this substitution is thoroughly demonstrated. Lastly, through a comparative analysis with existing discrete grid DOA estimation methods, we illustrate the superiority of the proposed approach, particularly under conditions of medium signal-to-noise ratio, varying numbers of snapshots, and close target angles. This method is less affected by the number of array elements, and is more usable in practices verified in real-world scenarios. Full article

Background/Objectives: Wnt signaling pathways are essential in various biological processes, including embryonic development and tissue homeostasis, and are implicated in many diseases. The R-Spondin (RSpo) family, particularly RSpo1, plays a significant role in modulating Wnt signaling. This study aims to explore how RSpo1 binding to astrocytic LGR6 receptors influences central nervous system (CNS) homeostasis, particularly in the context of inflammation. Methods: Human-induced pluripotent stem cell-derived astrocytes were treated with RSpo1 to assess its impact on inflammatory cytokine release. A proteomic analysis was conducted using a Human Cytokine Array Kit to measure differential protein expression. Pathway enrichment analysis was performed to identify affected signaling pathways. Results: RSpo1 treatment led to a suppression of inflammatory cytokines such as IL-10, IFN-γ, and IL-23 in astrocytes, while TNF-α and CXCL12 levels were increased. Pathway analysis revealed significant alterations in key signaling pathways, including cytokine–cytokine receptor interaction, chemokine signaling, and TNF signaling pathways, suggesting RSpo1’s role in modulating immune responses within the CNS. Conclusions: RSpo1 significantly influences inflammatory responses in astrocytes by modulating cytokine release and altering key signaling pathways. These findings enhance our understanding of the interaction between cell-specific Wnt signaling and CNS inflammation, suggesting potential therapeutic applications of RSpo1 in neuroinflammatory and neurodegenerative diseases. Full article

In order to address the inconsistency problem caused by parasitic backend wiring among multiple ramp generators and among multiple columns in large-array CMOS image sensors (CIS), this paper proposes a high-precision compensation technology combining average voltage technology, adaptive negative feedback dynamic adjustment technology, and digital correlation double sampling technology to complete the design of an adaptive ramp signals inconsistency calibration scheme. The method proposed in this article has been successfully applied to a CIS with a pixel array of 8192(H) × 8192(V), based on the 55 nm 1P4M CMOS process, with a pixel size of $10\times 10\mathsf{\mu }{\mathrm{m}}^2$. The chip area is 88(H) × 89(V) ${\mathrm{mm}}^2$, and the frame rate is 10 fps. The column-level analog-to-digital converter is a 12-bit single-slope analog-to-digital converter (SS ADC). The experimental results show that the ramp generation circuit proposed in this paper can reduce the inconsistency among the ramp signals to 0.4% LSB, decreases the column fixed pattern noise (CFPN) caused by inconsistent ramps of each column to 0.000037% (0.15 $e^{-}$), and increases the overall chip area and power consumption by only 0.6% and 0.5%, respectively. This method provides an effective solution to the influence of non-ideal factors on the consistency of ramp signals in large area array CIS. Full article

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach. Full article

α-Mangostin, a xanthone derivative extracted from the pericarp of the mangosteen fruit (Garcinia mangostana L.), has garnered significant attention for its potential as a natural anti-cancer agent. This review provides a comprehensive analysis of the current literature on the anti-cancer properties of α-mangostin across various cancer types. Through an extensive analysis of in vitro and in vivo studies, this review elucidates the multifaceted mechanisms underlying α-mangostin’s cytotoxicity, apoptosis induction through both intrinsic and extrinsic pathways, and modulation of key cellular processes implicated in cancer progression in a diverse array of cancer cells. It causes mitochondrial dysfunction, activates caspases, and regulates autophagy, endoplasmic reticulum stress, and oxidative stress, enhancing its anti-cancer efficacy. Moreover, α-mangostin exhibits synergistic effects with conventional chemotherapeutic agents, suggesting its utility in combination therapies. The ability of α-mangostin to inhibit cell proliferation, modulate cell cycle progression, and induce apoptosis is linked to its effects on key signaling pathways, including Akt, NF-κB, and p53. Preclinical studies highlight the therapeutic potential and safety profile of α-mangostin, demonstrating significant tumor growth inhibition without adverse effects on normal cells. In summary, understanding the molecular targets and mechanisms of action of α-mangostin is crucial for its development as a novel chemotherapeutic agent, and future clinical investigations are warranted to explore its clinical utility and efficacy in cancer prevention and therapy. Full article