The whisky industry heavily relies on peat as a key ingredient to impart distinctive smoky flavors to the final product. However, traditional methods for analyzing peat composition and quality are time-consuming and destructive, requiring extensive sample preparation. To address these challenges, we propose a novel nondestructive system for rapid and accurate peat analysis combining push-broom hyperspectral imaging (HSI), singular spectrum analysis (SSA), and machine learning. We introduce a faster SSA variant (SSA++) to overcome the high computational complexity of traditional SSA, enabling real-time processing of HSI data when captured in the push-broom manner. Comprehensive experiments have demonstrated the effectiveness of the proposed system, achieving a total phenol estimation of up to 99.31%R2. SSA++ maintains similar accuracy to SSA while significantly reducing computational time, enabling real-time performance. Our system offers a powerful tool for automated peat analysis, facilitating smart manufacturing and enhanced quality monitoring in the whisky industry.

Although text information has aided existing models to achieve promising results in open vocabulary object detection (OVD), the lack of semantic information has led to the difficulty in small objects detection (SOD). Moreover, such semantic gap also causes failure when matching texts and image features, resulting in false negative instances being detected. To address these issues, we propose a Cascade Open Vocabulary Detector (Cas-OVD), which builds upon existing multi-stage detection pipelines but specializes in text-vision alignment for small objects. In particular, we adapt a multi-refined region proposal network, guided by a non-sampled anchor strategy, to reduce the missing and false detections of small objects. Meanwhile, a deformable convolution network based feature conversion module is proposed to enhance the semantic information of small objects even the potential ones with low confidence. Unlike existing methods that rely on coarse-grained image-based features for image-text matching, Cas-OVD refines these features through a cascade alignment process, allowing each stage to build on the results of the previous one. This can progressively enhance the feature correlation between the image regions and the textual descriptions through successive error correction. On the joint BDD100K-SODA-D dataset, Cas-OVD achieved 17.95% AP$_{\mathrm{all}}$and 14.6% AP$_{\mathrm{s}}$, outperforming RegionCLIP by 3.5% AP$_{\mathrm{all}}$and 3.0% AP$_{\mathrm{s}}$, respectively. On the OV_COCO dataset, Cas-OVD has the 32.71% AP$_{\mathrm{all}}$and 17.26% AP$_{\mathrm{s}}$, surpassing the RegionCLIP by 6.6% AP$_{\mathrm{all}}$and 6.1% AP$_{\mathrm{s}}$, respectively.

Major accidents at substations are generally caused by a variety of safety hazards. However, the interactions between the various hazards within the substation, which result in a heterogeneous network of relationships, make it difficult for traditional generalised analytical models to accurately predict hidden dangers. To solve this problem, we propose a knowledge graph-based method for predicting substation accident hazards, designed to block potential accident causal pathways. Based on the knowledge graph of substation accident hazards, we enhance the network's understanding of anomalies in the complex substation environment through rule-based reasoning, while also leveraging the strong nonlinear fitting capabilities of neural networks to deeply explore the hidden relationships in the data, enabling the risk prediction of substation hazards and preventing major accidents. We validated our model using data from a provincial grid company over the past five years and achieved a prediction accuracy of 80%. This fully demonstrates the effectiveness of our approach in improving the prediction accuracy of substation hazards, and highlights its significant theoretical and practical value in enhancing the safety and stability of the entire substation system.

Measuring the purity of the metal powder is essential to maintain the quality of additive manufacturing products. Contamination is a significant concern, leading to cracks and malfunctions in the final products. Conventional assessment methods focus more on physical integrity rather than material composition and can be time-consuming. By capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture, and chemical composition to tackle this challenge. In this article, we explore the application of HSI in conjunction with machine learning for nondestructive inspection of metal powders. By employing near-infrared and visible HSI cameras, we introduce the utilization of HSI for this purpose. We delve into the technical challenges encountered and present detailed solutions through three case studies, including the establishment of a spectral dictionary, contamination detection, and band selection analysis. Our experimental results demonstrate the immense potential of HSI and its synergy with machine learning for nondestructive testing in powder metallurgy, particularly in meeting the requirements of industrial manufacturing environments.

User engagement in Virtual Reality (VR) games is crucial for creating immersive and captivating gaming experiences that meet the expectations of players. However, understanding and measuring these levels in VR games presents a challenge for game designers, as current methods, such as self-reports, may be limited in capturing the full extent of user engagement. Additionally, approaches based on biological signals to measure engagement in VR games present complications and challenges, including signal complexity, interpretation difficulties, and ethical concerns. This study explores body movements, as a novel approach to measure user engagement in VR gaming. We employ E4, emteqPRO, and off-the-shelf IMUs to measure the body movements from diverse participants engaged in multiple VR games. Further, we examine the simultaneous occurrence of player motivation and physiological responses to explore potential associations with body movements. Our findings suggest that body movements hold promise as a reliable and objective indicator of user engagement, offering game designers valuable insights on generating more engaging and immersive experiences.

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral- and spatial-domain feature extraction in hyperspectral images (HSIs). However, PCA itself suffers from low efficacy if no spatial information is combined, while 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this letter a PCA domain 2DSSA approach for spectral–spatial feature mining in HSI. Specifically, PCA and its variation, folded PCA (FPCA) are fused with the 2DSSA, as FPCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational cost can be significantly reduced while preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, our approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully validated the superiority of the proposed approach, in comparison to several state-of-the-art methods and deep learning models.

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology.

Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the noisy and water absorption bands discarded. In this study, a novel spectral-spatial feature mining framework, Multiscale Superpixelwise Prophet Model (MSPM), is proposed for noise-robust feature extraction and effective classification of the HSI. The prophet model is highly noise-robust for deeply digging into the complex structured features thus enlarging interclass diversity and improving intraclass similarity. First, the superpixelwise segmentation is produced from the first three principal components of an HSI to group pixels into regions with adaptively determined sizes and shapes. A multiscale prophet model is utilized to extract the multiscale informative trend components from the average spectrum of each superpixel. Taking the multiscale trend signal as the input feature, the HSI data are classified superpixelwisely, which is further refined by a majority vote based decision fusion. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and robustness of our MSPM model when benchmarked with eleven state-of-the-art algorithms, including six spectral-spatial methods and five deep learning ones. Besides, MSPM also shows superiority under limited training samples, due to the combined strategies of superpixelwise fusion and multiscale fusion. Our model has provided a useful solution for noise-robust feature extraction as it achieves superior HSI classification even from the uncorrected dataset without prefiltering the water absorption and noisy bands.

Underwater image enhancement (UIE) is essential for a high-quality underwater optical imaging system. While a number of UIE algorithms have been proposed in recent years, there is little study on image quality assessment (IQA) of enhanced underwater images. In this paper, we conduct the first crowdsourced subjective IQA study on enhanced underwater images. We chose ten state-of-the-art UIE algorithms and applied them to yield enhanced images from an underwater image benchmark. Their latent quality scales were reconstructed from pair comparison. We demonstrate that the existing IQA metrics are not suitable for assessing the perceived quality of enhanced underwater images. In addition, the overall performance of 10 UIE algorithms on the benchmark is ranked by the newly proposed simulated pair comparison of the methods.

Effective acquisition, analysis and reconstruction of multi‐modal data such as colour and multi‐/hyper‐spectral imagery is crucial in smart camera applications, where wavelet‐based coding and compression of images are highly demanded. Many existing discrete wavelet filtering banks have fixed coefficients hence their performance is highly dependent on the signal/image being processed. To tackle this problem, a unified framework is proposed in this study, which can produce a series of discrete wavelet filtering banks, where many existing discrete wavelet filtering banks become special cases of the framework. For each generated filtering bank, it consists of two decomposition filters and two reconstruction filters through an optimisation process. The efficacy of the filtering banks produced by the framework has been validated in two case studies, including colour image decomposition and reconstruction, and hyperspectral image classification. Comprehensive experiments have demonstrated the superior performance of the proposed framework, which will benefit the efficacy of smart camera and camera network applications.

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude (alpha), mean (mu), and standard deviation (sigma) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma, alpha and mu values. The paper concludes with a number of open questions and outlines future research directions.

Dimensionality reduction is of high importance in hyperspectral data processing, which can effectively reduce the data redundancy and computation time for improved classification accuracy. Band selection and feature extraction methods are two widely used dimensionality reduction techniques. By integrating the advantages of the band selection and feature extraction, the authors propose a new method for reducing the dimension of hyperspectral image data. First, a new and fast band selection algorithm is proposed for hyperspectral images based on an improved determinantal point process (DPP). To reduce the amount of calculation, the dual‐DPP is used for fast sampling representative pixels, followed by k‐nearest neighbour‐based local processing to explore more spatial information. These representative pixel points are used to construct multiple adjacency matrices to describe the correlation between bands based on mutual information. To further improve the classification accuracy, two‐dimensional singular spectrum analysis is used for feature extraction from the selected bands. Experiments show that the proposed method can select a low‐redundancy and representative band subset, where both data dimension and computation time can be reduced. Furthermore, it also shows that the proposed dimensionality reduction algorithm outperforms a number of state‐of‐the‐art methods in terms of classification accuracy.