Explore the vast range of titles available.

Nowadays digital replicas of artefacts belonging to the Cultural Heritage (CH) are one of the most promising innovations for museums exhibitions, since they foster new forms of interaction with collections, at different scales. However, practical digitization is still a complex task dedicated to specialized operators. Due to these premises, this paper introduces a novel approach to support non-experts working in museums with robust, easy-to-use workflows based on low-cost widespread devices, aimed at the study, classification, preservation, communication and restoration of CH artefacts. The proposed methodology introduces an automated combination of acquisition, based on mobile equipment and visualization, based on Real-Time Rendering. After the description of devices used along the workflow, the paper focuses on image pre-processing and geometry processing techniques adopted to generate accurate 3D models from photographs. Assessment criteria for the developed process evaluation are illustrated. Tests of the methodology on some effective museum case studies are presented and discussed.

Tunable diode laser absorption spectroscopy technology (TDLAS) has been widely applied in gaseous component analysis based on gas molecular absorption spectroscopy. When dealing with molecular absorption signals, the desired signal is usually interfered by various noises from electronic components and optical paths. This paper introduces TDLAS-specific signal processing issues and summarizes effective algorithms so solve these.

Three-dimensional Gaussian splatting has emerged as a mainstream method in the field of new viewpoint synthesis due to its outstanding performance. However, its generation quality typically degrades significantly when input viewpoints are sparse. The introduction of InstantSplat further improved new viewpoint generation in sparse viewpoint scenarios. Nevertheless, these methods produce suboptimal results in sparse viewpoint scenes with noise and no camera prior. To address this issue, we propose Denoise-GS, a two-round optimization framework combining N2V-UNet denoising with InstantSplat rendering. First, Noise2Void performs self-supervised denoising on the input image. Next, pose grouping is conducted based on InstantSplat rendered results. Finally, a second round of refinement is applied to the UNet through a joint loss function. The final denoised result is then re-rendered to achieve a higher-quality output image. To simulate a real noisy environment, we added Gaussian noise to the input images. Tests on multiple datasets show that, compared with other mainstream methods, our approach produces images with higher PSNR and SSIM. The method performs well in novel view generation when the input images are sparse and noisy, providing an innovative and practical solution for three-dimensional reconstruction.

Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect image quality, but there is currently no unified indicator to evaluate the image quality of event streams. This paper proposes a method to eliminate background activity, and proposes a method and performance index for evaluating filter performance: noise in real (NIR) and real in noise (RIN). The lower the value, the better the filter. This evaluation method does not require fixed pattern generation equipment, and can also evaluate filter performance using natural images. Through comparative experiments of the three filters, the comprehensive performance of the method in this paper is optimal. This method reduces the bandwidth required for DVS data transmission, reduces the computational cost of target extraction, and provides the possibility for the application of DVS in more fields.

•A linear transformer-based deep learning model for MEG ERF denoising.•DeepReducer enhances the SNR of ERFs, outperforming traditional methods.•The model reduces experimental trials by up to 80 %, streamlining data acquisition.•DeepReducer lowers participant stress and minimizes artifacts for reliable data. Measuring event-related magnetic fields (ERFs) in magnetoencephalography (MEG) is crucial for investigating perceptual and cognitive information processing in both neuroscience research and clinical practice. However, the magnitude of the ERF in cortical sources is comparable to the noise in a single trial. Consequently, numerous repetitive recordings are needed to distinguish these sources from background noise, requiring lengthy time for data acquisition. Herein, we introduce DeepReducer, a linear transformer-based deep learning model designed to reliably and efficiently denoise ERFs, thereby reducing the number of required trials. DeepReducer was trained on a mix of limited-trial and multi-trial averaged ERFs, employing mean squared error as the loss function to effectively capture and model the complex signal fluctuations inherent in MEG recordings. Validation on both semi-synthetic and experimental task-related MEG data showed that DeepReducer outperforms conventional trial-averaging techniques, significantly improving the signal-to-noise ratio of ERFs and reducing source localization errors. The practical significance of DeepReducer encompasses optimizing MEG data acquisition by reducing participant stress (particularly for patients) and minimizing associated artifacts.

Eye movement analysis serves as a powerful tool for early dyslexia detection. Signal quality issues, signal degradation from noise, drift, and motion artifacts, remain a major obstacle to reliable eye movement analysis. To overcome these challenges and enhance the quality of eye movement data, we proposed a two-stage framework consisting of primary signal denoising and Adaptive recursive enhancement. In the first stage, denoising using filters such as the Savitzky-Golay (SG) filter, the Infinite Impulse Response (IIR) filter, the finite impulse response (FIR) filter, and the Median filter are applied, with the output exhibiting the highest signal-to-noise ratio (SNR) selected for further analysis. If initial filtering is unable to improve SNR enough, the pipeline moves on to the second stage, where Kalman filters with the Constant Velocity Model, Bayesian Model, and Constant Acceleration Model refine the signal while preserving features like saccades. Quantitative evaluation of the pipeline using metrics, the IIR filter had the highest SNR in the first stage, while CAMKF had the highest SNR in the second stage. Compared to the signal filter method, the two-stage approach consistently yielded higher SNR and better preservation of eye movement features, improving signal quality and enabling more precise dyslexia related analysis.

Wafer defect recognition is an important process of chip manufacturing. As different process flows can lead to different defect types, the correct identification of defect patterns is important for recognizing manufacturing problems and fixing them in good time. To achieve high precision identification of wafer defects and improve the quality and production yield of wafers, this paper proposes a Multi-Feature Fusion Perceptual Network (MFFP-Net) inspired by human visual perception mechanisms. The MFFP-Net can process information at various scales and then aggregate it so that the next stage can abstract features from the different scales simultaneously. The proposed feature fusion module can obtain higher fine-grained and richer features to capture key texture details and avoid important information loss. The final experiments show that MFFP-Net achieves good generalized ability and state-of-the-art results on real-world dataset WM-811K, with an accuracy of 96.71%, this provides an effective way for the chip manufacturing industry to improve the yield rate.

Channel estimation of an orthogonal frequency division multiplexing (OFDM) system based on compressed sensing can effectively reduce the pilot overhead and improve the utilization rate of spectrum resources. The traditional SAMP algorithm with a fixed step size for sparse channel estimation has the disadvantages of a low estimation efficiency and limited estimation accuracy. An Improved SAMP (ImpSAMP) algorithm is proposed to estimate the channel state information of the OFDM system. In the proposed ImpSAMP algorithm, the received signal is firstly denoised based on the energy-detection method, which can reduce the interferences on channel estimation. Furthermore, the step size is adjusted dynamically according to the l2 norm of difference between two estimated sparse channel coefficients of adjacent phases to estimate the sparse channel coefficients quickly and accurately. In addition, the double threshold judgment is adopted to enhance the estimation efficiency. The simulation results show that the ImpSAMP algorithm outperforms the traditional SAMP algorithm in estimation efficiency and accuracy.

Abstract Soundscapes contain rich ecological information, offering insights into both biodiversity and ecosystem dynamics. However, the sheer volume of data produced by passive acoustic monitoring presents significant challenges for scalable analysis and ecological interpretation. While convolutional neural networks (CNNs) have advanced species classification in bioacoustics, they often struggle with identifying acoustic targets in acoustic space and quantifying soundscapes' characteristics. In this study, we propose a novel spectrogram‐to‐spectrogram translation framework based on generative adversarial networks (GANs) to isolate and quantify acoustic sources within soundscape recordings. Our method is trained on paired spectrogram images: original full‐spectrogram representations and target spectrogram representations containing only the vocalizations of specific sound labels. This design enables the model to learn source‐specific mappings and perform both the species and community‐level separation of acoustic components in soundscape recordings. We developed and evaluated two GAN‐based models: a species‐level GAN targeting eight avian species, and a community‐level GAN distinguishing among avian, insect and anthropogenic sound sources. The models were trained and tested using soundscape recordings collected from the Yaoluoping National Nature Reserve, eastern China. The species‐level model achieved a mean F1 score of 0.76 for pixel‐wise detection, while the community‐level model reached 0.79 across categories. In addition to precise temporal‐spectral localization, our approach captures sources' acoustic occupancy and frequency distribution patterns, offering deeper ecological insight. Compared to baseline CNN classifiers, our model achieved a mean F1 score of 0.97, demonstrating comparable classification performance to ResNet50 (0.95) and VGG16 (0.98) across multiple species. Our GAN approach for extracting sound sources also significantly outperformed conventional methods in denoising and source separation, as indicated by lower image‐level mean squared error. These results demonstrate the utility of GANs in advancing ecoacoustic analyses and biodiversity monitoring. By enabling robust source separation and fine‐resolution signal mapping, the proposed approach contributes a scalable and transferable tool for soundscape quantification.

In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered. To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals. The denoising performance of DWT is discussed by several processing parameters, including the type of wavelet, decomposition level, thresholding method, and threshold selection rules. To overcome the disadvantages of the traditional hard- and soft-thresholding methods, an improved thresholding technique called the sigmoid function-based thresholding scheme is presented. The procedure is validated by using four benchmarks signals with three degrees of degradation as well as a real measured signal obtained from a three-story reinforced concrete scale model shaking table experiment. The performance of the proposed method is evaluated by computing the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) after denoising. Results reveal that the proposed method offers superior performance than the traditional methods no matter whether the signals have heavy or light noises embedded.