Search Results (84)

The friction-induced stick-slip vibration (FISSV) generated by intense friction between the brake disc and brake pads of high-speed trains is a critical issue affecting braking stability, the service life of foundational braking components, and ride comfort. The floating friction block structure, which effectively regulates interfacial contact characteristics through the elastic deformation of disc springs, thereby improving tribological behavior, represents an effective approach for mitigating FISSV. However, the topic of how to design the floating structure of the friction block to produce the best suppression impact on FISSV emerges, using the choice of disc spring material as an example. Thus, the purpose of this study is to look at how disc spring material affects stick-slip vibration (SSV) at the high-speed train floating brake interface. Four typical disc spring materials—304 stainless steel, Mubea-specific spring steel, 50CrVA high-alloy spring steel, and 60Si₂MnA silicon-manganese spring steel—were selected. Through braking tribological tests and explicit dynamics-wear coupling simulations, the effects of material differences on interfacial friction-wear characteristics and SSV behavior were systematically studied. The findings show that the stiffness of the disc spring material greatly influences the dynamic responsiveness of the system and the contact pressure distribution at the braking interface, elasticity, and damping characteristics. 60Si₂MnA spring steel, owing to its excellent elastic recovery and load equalization capability, promoted the formation of uniformly dispersed medium-to-small contact platforms on the interface, resulting in the mildest wear. Concurrently, its system vibration energy exhibited a more dispersed distribution in the frequency domain, with low SSV intensity and weak nonlinear behavior, demonstrating the best comprehensive performance. Materials with poorer compatibility, such as 304 stainless steel, tended to cause localized stress concentration, exacerbating wear and intensifying severe high-frequency SSV. The influence mechanism of disc spring material at the interface is shown by this work, providing an important basis for material optimization and vibration suppression design in floating brake pad structures. Full article

Ultra-high-field 7 T time-of-flight MR angiography (TOF-MRA) provides detailed visualization of intracranial vasculature, but quantitative analysis is sensitive to intensity inhomogeneity and non-standardized post-processing. We present TOFR-VET (Time-of-Flight Radiomics and Vessel Enhancement Toolkit), a unified MATLAB workflow that integrates vessel enhancement, objective image quality evaluation (PSNR, SSIM, entropy, contrast improvement index (CII), and brightness preservation factor (BPF)), and radiomic feature extraction within a single as a proposed methodological framework for consistent analysis. Using 7 T TOF-MRA datasets (n = 120 subjects), we comparatively evaluated histogram-based and adaptive enhancement approaches and quantified their impact on image quality behavior and radiomic feature sensitivity. Adaptive Gamma Correction with Weighting Distribution (AGCWD), as one of the methods tested, resulted in higher fidelity-to-baseline metrics than standard histogram equalization (HE), demonstrated by improved PSNR metrics (23.54 dB versus 15.82 dB; increase of 7.72 dB) along with observed complementary trends in SSIM and BPF. Brightness-preserving methods maintained BPF values close to unity. Because enhancement methods intentionally reshape intensity distributions, PSNR/SSIM are interpreted here as structural deviation indicators relative to the original image rather than absolute measures of diagnostic quality. Quantitative analysis of 120 subjects using one-way ANOVA revealed that while Laplacian of Gaussian (LoG) filtering provides the highest contrast improvement for visual vessel continuity (CII = 1.65 ± 0.22, p < 0.001), Adaptive Gamma Correction (AGCWD) is superior for radiomic pipelines, maintaining an Intraclass Correlation Coefficient (ICC) of 0.94. These findings provide a specific decision-making framework for standardizing 7T TOF-MRA preprocessing based on the desired diagnostic or quantitative objective. Full article

Breast cancer is one of the most fatal cancers for women and requires accurate cancer diagnosis technology. In this research, we incorporated advanced image preprocessing methods, including histogram equalization (HE), stain normalization, intensity normalization, and Richard normalization, along with deep learning, to enhance invasive ductal carcinoma prognosis. Additionally, we evaluated the effectiveness of HE for different image contrast enhancement and model performance optimization methods. The Residual network with 50 layers, the densely connected convolutional network, and the efficient neural network architecture were tested on a publicly available histopathological image dataset. DenseNet showed an accuracy of 0.5523 with Richard normalization and Stain Normalization. ResNet-50 showed an accuracy of 0.5500 when using histogram equalization as a pre-processing step. The results proved that histogram equalization is effective in relieving contrast and feature extraction, which are important in class medical image analysis and dealing with class imbalance problems. The results demonstrate the feasibility of artificial intelligence-based solutions in improving breast cancer prognosis through these inexpensive and efficient prognosis tools. Full article

Lung cancer is one of the most common causes of death from cancer around the world, and catching it early through computed tomography (CT) scans can drastically improve survival. However, automated classification of pulmonary nodule candidates is hard because images do not all have the same intensity across scanners and protocols, resulting in inconsistent performance, more false positives (FP), and a ceiling on how much deep learning models work in an average clinic. In this work, we tackle this by introducing a preprocessing step that corrects intensity differences before feeding images into classification models. We use Contrast-Limited Adaptive Histogram Equalization (CLAHE), but with its key parameters tuned automatically via a modified version of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This helps to boost local contrast adaptively, keeps important anatomical details intact, and cuts down on noise. We tested the approach on the public LUNA16 dataset, first checking image quality (Peak Signal-to-Noise Ratio (PSNR) around 53 dB and Structural Similarity Index (SSIM) of 0.9, better than standard methods), then training three popular deep models—namely, ResNet-50, EfficientNet-B0, and InceptionV3—with CutMix augmentation for better generalization. On the enhanced images, ResNet-50 achieved up to 99.0% classification accuracy with substantially less FP than when using the raw scans. Taken together, these results demonstrate that intelligent and optimized preprocessing can effectively mitigate intensity variations via deep learning for lung nodule detection, thus coming closer to realizing the practical toolbox of computer-aided diagnosis in routine clinical practice. Full article

Solid-state lighting, especially light-emitting diodes (LEDs), is revolutionizing indoor lighting due to its energy efficiency, long lifespan, low heat output, and enhanced color rendering. LEDs can quickly adjust light intensity, enabling the development of visible light communication (VLC) technology. However, the modulation bandwidth of phosphor-converted white LEDs commonly used for illumination is limited, potentially affecting the speed of the VLC links. This paper presents a receiver-side equalization technique to overcome bandwidth limitations in VLC links due to LEDs. The proposed approach utilizes a novel transimpedance amplifier with an embedded T-network shunt-feedback equalizer (TIA-TE) to introduce adjustable high-frequency peaking in the TIA’s frequency response. By incorporating this peaking, the system’s bandwidth is extended without sacrificing important performance parameters like gain, noise, or power dissipation. The TIA-TE is followed by a main amplifier and a standalone continuous-time linear equalizer (CTLE) for further signal conditioning, while a 50 Ω buffer interfaces the receiver with measurement equipment. Post-layout simulations in a 0.35 µm CMOS process validate the approach. Using a 4 pF photodiode, the system bandwidth was initially limited by the LED’s 3 MHz modulation bandwidth. The proposed TIA-TE extends the bandwidth to 8.4 GHz without sacrificing the gain or power dissipation. The subsequent CTLE further extends the bandwidth to 14 MHz. The receiver front end achieves a mid-band transimpedance of 110 dBΩ and an input-referred noise current of 7.2 nA_rms, while dissipating 2.48 mW (excluding the 50 Ω buffer). Simulated 28 Mb/s NRZ eye diagrams demonstrate the feasibility of the proposed TIA-TE architecture for LED-limited VLC links. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article

Low contrast is a frequent challenge in image analysis, especially within medical imaging and highly saturated scenes. To address this issue, we present a nonlinear transformation for local contrast enhancement in digital images. Our method adapts the hyperbolic tangent function using two parameters: one to select the intensity range for modification and another to control the degree of enhancement. This approach outperforms conventional histogram-based techniques such as histogram equalization and specification in local contrast enhancement, without increasing computational cost, and produces smooth, artifact-free results in user-defined regions of interest. In addition, the proposed method was compared with CLAHE in MRIs, showing that, unlike CLAHE, the proposed method does not enhance the noise present in the background of the image. Furthermore, in deep learning contexts where dataset size is often limited, our method could serve as an effective data augmentation tool—generating varied contrast images while preserving anatomical structures, which improves neural network training for brain tumor detection in magnetic resonance imaging. The ability to manipulate local contrast may offer a pathway toward better interpretability of convolutional neural networks, as targeted contrast adjustments allow researchers to probe model sensitivity and enhance the explainability of classification and detection mechanisms. Full article

Background/Objectives: Lung cancer is the leading cause of cancer-related mortality worldwide, and early detection using low-dose computed tomography (LDCT) substantially improves survival outcomes. However, variations in CT acquisition and reconstruction parameters including Hounsfield Unit (HU) calibration, reconstruction kernels, slice thickness, radiation dose, and scanner vendor introduce significant intensity and contrast variability that undermine the robustness and generalizability of deep-learning (DL) systems. Methods: This systematic review followed PRISMA 2020 guidelines and searched PubMed, Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar for studies published between 2020 and 2025. A total of 100 eligible studies were included. The review evaluated preprocessing and harmonization strategies aimed at mitigating CT intensity variability, including perceptual contrast enhancement, HU-preserving normalization, physics-informed harmonization, and DL-based reconstruction. Results: Perceptual methods such as contrast-limited adaptive histogram equalization (CLAHE) enhanced nodule conspicuity and reported sensitivity improvements ranging from 10 to 15% but frequently distorted HU values and reduced radiomic reproducibility. HU-preserving approaches including HU clipping, ComBat harmonization, kernel matching, and physics-informed denoising were the most effective, reducing cross-scanner performance degradation, specifically in terms of AUC or Dice score loss, to below 8% in several studies while maintaining quantitative integrity. Transformer and hybrid CNN–Transformer architectures demonstrated superior robustness to acquisition variability, with observed AUC values ranging from $0.90$ to $0.92$ compared with $0.85$–$0.88$ for conventional CNN models. Conclusions: The evidence indicates that standardized HU-faithful preprocessing pipelines, harmonization-aware modeling, and multi-center external validation are essential for developing clinically reliable and vendor-agnostic AI systems for lung-cancer screening. However, the synthesis of results is constrained by the heterogeneous reporting of acquisition parameters across primary studies. Full article

Promoting the coupled and coordinated development of new-type urbanization and rural revitalization is important for achieving high-quality and sustainable growth in China. This study follows a people-centered and coordinated development approach and is aligned with the Sustainable Development Goals (SDGs). It builds a comprehensive evaluation framework for the two systems and measures and interprets their coupling and coordination. On this basis, and under the background of China’s territorial spatial planning, the study draws implications for land and spatial governance. The core of the study is to answer the following questions: What are the spatiotemporal patterns of the coupling coordination level between new-type urbanization and rural revitalization in China from 2014 to 2023? How has the coordination of their development speed evolved? What are the main sources of regional differences? Which factors are the key drivers that promote coordinated development between the two systems? The main findings are as follows. (1) The national coupling coordination degree increases steadily. Spatially, there is a pattern of “eastern region leading, central and northeastern regions catching up, and western region showing internal divergence”. This pattern is consistent with differences in development intensity and accessibility across regions. (2) From 2019 to 2023, the coordination of development speed improved in most provinces. A few developed or special provinces show short-term mismatch, which may reflect timing gaps between land-use controls and the provision of public services. (3) Gaps between regions are the main source of overall differences, and there is a trend toward convergence. This is in line with interregional equalization and the narrowing of efficiency gaps. (4) Well-being of residents, social development, and digital innovation are the core driving forces. Digital inclusive finance and the intensity of parcel delivery services also provide important support. There are clear interaction effects among the driving factors, and these effects are stronger in areas where planning improves accessibility and reduces transaction costs. Full article

This paper proposes a new planning method for a cyclic production of many different products with steady demand and variable production rates, which minimises energy consumption while reducing and equalising power demand. The problem is modelled as the Economic Lot Scheduling Problem (elsp), with a common production cycle for all products. This paper shows that the problem can be optimally solved by a general-purpose mathematical programming solver in a short time by reformulating the general non-linear model into a Mixed-Integer Quadratically Constrained Programming (miqcp) model. This way, there is no need to develop a specialised algorithm, which requires a high level of expertise and is very labour-intensive. The proposed approach is also the only method that allows finding optimal solutions for the general case of the common-cycle elsp with variable production rates. For a problem instance known from the literature, the optimal solution ensured a reduction in the power demand cost by 10.7%, and in the total cost by 3.3%. Moreover, experiments proved that production rate lower bounds are critical for the choice of solution. Full article

Environmental factors like light, photoperiod, temperature, and water are vital for crop growth, and even slight deviations from their optimal ranges can cause seedling stress and reduce yield. Therefore, this study aimed to quantify seedling stress symptoms using image features analysis under varying light, photoperiod, temperature, and water conditions. Seedlings were grown under controlled low, normal, and high environmental conditions. Light intensity at 50 µmol m⁻² s⁻¹ (low), 250 µmol m⁻² s⁻¹ (normal), and 450 µmol m⁻² s⁻¹ (high), photoperiod cycles, 8/16 h (day/night) (low), 10/14 h (day/night) (normal), and 16/8 h (day/night) (high) day/night, temperature at 20 °C (low), 25 °C (normal), and 30 °C (high), and water availability at 1 L per day (optimal), 1 L every two days (moderate stress), and 1 L every three days (severe stress) were applied for 15 days. Commercial low-cost RGB, thermal, and depth sensors were used to collect data every day. A total of 1080 RGB images, which were pre-processed with histogram equalization and filters (Median and Gaussian), were used for noise reduction to minimize illumination effects. Morphological, color, and texture features were then analyzed using ANOVA (p < 0.05) to assess treatment effects. The result shows that the maximum canopy area for tomato was 115,226 pixels, while lettuce’s maximum plant height was 9.28 cm. However, 450 µmol m⁻² s⁻¹ light intensity caused increased surface roughness, indicating stress-induced morphological alteration. The analysis of Combined Stress Index (CSI) values indicated that the highest stress levels were 50% for pepper, 55% for tomato, 62% for cucumber, 55% for watermelon, 50% for lettuce, and 50% for pak choi. The findings showed that image-based stress detection enables precise environmental control and improves early-stage crop management. Full article

In this paper, we demonstrate a receiver-side lookup table (Rx-side LUT) equalization method for high-speed short-reach intensity modulation and direct detection (IM/DD) transmission systems, which alleviates the computational complexity of neural network-based equalization algorithms. Compared to conventional pre-equalization techniques applied at the transmitter side, which utilize distortion correction values stored in LUTs derived from the transmitted symbols and their corresponding recovered counterparts, the Rx-side LUT relies solely on receiver-side data. The received data to be equalized serves as the index of the LUT, with a nearest-neighbor algorithm employed to find the element closest to the index and then return the corresponding equalization result from the table. With a lightweight lookup process, the proposed method releases the computation complexity of neural network-based equalization algorithms by replacing the computationally intensive operations performed during the inference phase. Experimental results indicate that compared to baseline fully connected neural network (FCNN) and gated recurrent unit (GRU) equalization, the Rx-side LUT could decrease the algorithm execution time by 25.5% and 34.6% for 100 GBaud and 22.8% and 36.9% for 112 GBaud PAM4 signals, respectively, while maintaining comparable system performance. The proposed scheme provides a low-complexity solution for high-speed, low-cost IM/DD optical interconnects. Full article

Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff (water accumulation). These two physical phenomena become intertwined in the received signals, resulting in severe feature ambiguity. This not only greatly limits the accuracy of environmental sensing but also hinders communication systems from performing effective channel compensation. How to disentangle these combined effects from a single wireless link represents a fundamental scientific challenge for achieving high-precision wireless environmental sensing and ensuring communication reliability under harsh conditions. To address this challenge, we propose a novel signal processing framework that aims to effectively decouple the effects of rainfall and surface runoff from Channel State Information (CSI) collected using commercial Wi-Fi devices. The core idea of our method lies in first constructing a two-dimensional CSI spatiotemporal spectrogram from continuously captured multicarrier CSI data. This spectrogram enables high-resolution visualization of the unique “fingerprints” of different physical effects—rainfall manifests as smooth background attenuation, whereas surface runoff appears as sparse high-frequency textures. Building upon this representation, we design and implement a Dual-Decoder Convolutional Autoencoder deep learning model. The model employs a shared encoder to learn the mixed CSI features, while two distinct decoder branches are responsible for reconstructing the global background component attributed to rainfall and the local texture component associated with surface runoff, respectively. Based on the decoupled signal components, we achieve simultaneous and highly accurate estimation of rainfall intensity (mean absolute error below 1.5 mm/h) and surface water accumulation (detection accuracy of 98%). Furthermore, when the decoupled and refined channel estimates are applied to a communication receiver for channel equalization, the Bit Error Rate (BER) is reduced by more than one order of magnitude compared to conventional equalization methods. Full article

Traditional GrabCut algorithms are limited by their reliance on manual intervention, often resulting in segmentation errors and missed detections, particularly against complex backgrounds. This study addresses these limitations by introducing the Auto Trademark Cut (AT-Cut), an advanced automated trademark image-segmentation method built upon an enhanced GrabCut framework. The proposed approach achieves superior performance through three key innovations: Firstly, histogram equalization is applied to the entire input image to mitigate noise induced by illumination variations and other environmental factors. Secondly, state-of-the-art object detection techniques are utilized to precisely identify and extract the foreground target, with dynamic region definition based on detection outcomes to ensure heightened segmentation accuracy. Thirdly, morphological erosion and dilation operations are employed to refine the contours of the segmented target, leading to significantly improved edge segmentation quality. Experimental results indicate that AT-Cut enables efficient, fully automated trademark segmentation while minimizing the necessity for labor-intensive manual labeling. Evaluation on the public Real-world Logos dataset demonstrates that the proposed method surpasses conventional GrabCut algorithms in both boundary localization accuracy and overall segmentation quality, achieving a mean accuracy of 90.5%. Full article

Background/Objective: Multimodal image registration plays a critical role in modern medical imaging, enabling the integration of complementary modalities such as positron emission tomography (PET) and computed tomography (CT). This study compares the performance of three widely used image registration techniques—Demons Image Registration with Modality Transformation, Free-Form Deformation using the Medical Image Registration Toolbox (MIRT), and MATLAB Intensity-Based Registration—in terms of improving PET/CT image alignment. Methods: A total of 100 matched PET/CT image slices from a clinical scanner were analysed. Preprocessing techniques, including histogram equalisation and contrast enhancement (via imadjust and adapthisteq), were applied to minimise intensity discrepancies. Each registration method was evaluated under varying parameter conditions with regard to sigma fluid (range 4–8), histogram bins (100 to 256), and interpolation methods (linear and cubic). Performance was assessed using quantitative metrics: root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), the Pearson correlation coefficient (PCC), and standard deviation (STD). Results: Demons registration achieved optimal performance at a sigma fluid value of 6, with an RMSE of 0.1529, and demonstrated superior computational efficiency. The MIRT showed better adaptability to complex anatomical deformations, with an RMSE of 0.1725. MATLAB Intensity-Based Registration, when combined with contrast enhancement, yielded the highest accuracy (RMSE = 0.1317 at alpha = 6). Preprocessing improved registration accuracy, reducing the RMSE by up to 16%. Conclusions: Each registration technique has distinct advantages: the Demons algorithm is ideal for time-sensitive tasks, the MIRT is suited to precision-driven applications, and MATLAB-based methods offer flexible processing for large datasets. This study provides a foundational framework for optimising PET/CT image registration in both research and clinical environments. Full article