Search Results (55)

The segmentation and classification of color are crucial stages in image processing, computer vision, and pattern recognition, as they significantly impact the results. The diverse, hand-labeled datasets in the literature are applied for monochromatic or color segmentation in specific domains. On the other hand, synthetic datasets are generated using statistics, artificial intelligence algorithms, or generative artificial intelligence (AI). This last one includes Large Language Models (LLMs), Generative Adversarial Neural Networks (GANs), and Variational Autoencoders (VAEs), among others. In this work, we propose VitralColor-12, a synthetic dataset for color classification and segmentation, comprising twelve colors: black, blue, brown, cyan, gray, green, orange, pink, purple, red, white, and yellow. VitralColor-12 addresses the limitations of color segmentation and classification datasets by leveraging the capabilities of LLMs, including adaptability, variability, copyright-free content, and lower-cost data—properties that are desirable in image datasets. VitralColor-12 includes pixel-level classification and segmentation maps. This makes the dataset broadly applicable and highly variable for a range of computer vision applications. VitralColor-12 utilizes GPT-5 and DALL·E 3 for generating stained-glass images. These images simplify the annotation process, since stained-glass images have isolated colors with distinct boundaries within the steel structure, which provide easy regions to label with a single color per region. Once we obtain the images, we use at least one hand-labeled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation. This process enables us to automatically label a classification dataset and generate segmentation maps. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color—which include 9,509,524 labeled pixels, 1,794,758 of which are unique. These annotated pixels are represented by RGB, HSL, CIELAB, and YCbCr values, enabling a detailed color analysis. Moreover, VitralColor-12 offers features that address gaps in public resources such as violin diagrams with the frequency of colors across images, histograms of channels per color, 3D color maps, descriptive statistics, and standardized metrics, such as ΔE76, ΔE94, and CIELAB Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification. Full article

The escalating global threat of antimicrobial resistance (AMR) necessitates innovative therapeutic strategies beyond traditional antibiotic development. Drug repurposing offers a rapid, cost-effective approach by identifying new antibacterial applications for existing non-antibiotic drugs with established safety profiles. Emerging evidence indicates that diverse classes of non-antibiotic drugs, including non-steroidal anti-inflammatory drugs (NSAIDs), statins, antipsychotics, calcium channel blockers and antidepressants, exhibit intrinsic antibacterial activity, or potentiate antibiotic efficacy. This review critically explores the mechanisms by which drugs that are not recognised as antibiotics exert antibacterial effects, including efflux pump inhibition, membrane disruption, biofilm inhibition, and quorum sensing interference. We discuss specific examples that demonstrate reductions in minimum inhibitory concentrations (MICs) of antibiotics when combined with these drugs, underscoring their potential as antibiotic adjuvants. Furthermore, we examine pharmacokinetic considerations, toxicity challenges, and clinical feasibility for repurposing these agents as standalone antibacterials or in combination therapies. Finally, we highlight future directions, including the integration of artificial intelligence and machine learning to prioritise drug candidates for repurposing, and the development of targeted delivery systems to enhance bacterial selectivity while minimising host toxicity. By exploring the overlooked potential of non-antibiotic drugs, this review seeks to stimulate translational research aimed at leveraging these agents in combating resistant bacterial infections. Nonetheless, it is crucial to acknowledge that such drugs may also pose unintended risks, including gut microbiota disruption and facilitation of resistance development. Hence, future research should pursue these opportunities with equal emphasis on efficacy, safety, and resistance mitigation. Full article

The emergence of 6G wireless systems marks a paradigm shift toward intelligent, context-aware networks that can adapt in real-time to their environment. Within this landscape, Sensing-Assisted Communication (SAC) emerges as a key enabler, integrating perception into the communication control loop to enhance reliability, beamforming accuracy, and system responsiveness. Unlike prior surveys that treat SAC as a subfunction of Integrated Sensing and Communication (ISAC), this work offers the first dedicated review of SAC in Millimeter-Wave (mmWave) and Sub-Terahertz (Sub-THz) systems, where directional links and channel variability present core challenges. SAC encompasses a diverse set of methods that enable wireless systems to dynamically adapt to environmental changes and channel conditions in real time. Recent studies demonstrate up to 80% reduction in beam training overhead and significant gains in latency and mobility resilience. Applications include predictive beamforming, blockage mitigation, and low-latency Unmanned Aerial Vehicle (UAV) and vehicular communication. This review unifies the SAC landscape and outlines future directions in standardization, Artificial Intelligence (AI) integration, and cooperative sensing for next-generation wireless networks. Full article

This paper presents a comprehensive investigation about the use of electroencephalography (EEG) signals for classifying music stimuli through an artificial neural network (ANN). Employing the 16-channel OpenBCI CytonDaisy sensor, EEG data were gathered from participants while they listened to a variety of music tracks. This study examines the impact of varying time window lengths on classification accuracy, evaluates the neural network’s performance with different time- and frequency-domain features, analyzes the influence of diverse music on brain activity patterns, and reveals how songs of different styles affect various subjects. For the five subjects involved in the study, the recognition rate of the model fluctuated between 61% and 96%. The findings indicate that longer time windows, particularly 30 s, result in the highest classification accuracy. Despite the relatively high recognition rate, this study also highlights the issue of intra-individual variability. A substantial decline in performance can be observed when testing the model on data collected from the same person on a different day, underscoring the challenges posed by inter-session variability. Full article

Background/Objectives: Sex determination and differentiation exhibit remarkable molecular diversity across taxa, driven by genetic, epigenetic, and environmental factors. Invertebrates with sequential hermaphroditism, such as the Pacific oyster (Magallana gigas), represent a poorly understood system despite their role as keystone species and contribution to a substantial aquaculture industry. Methods: To identify sex-related molecular markers during gametogenesis, we repeatedly sampled hemolymph from artificially conditioned oysters over two months, and sex phenotypes were assigned at the end of the experiment by biopsy. Results: RNA-sequencing analysis of five males and five females revealed subtle yet consistent sex-specific transcriptional signatures in hemolymph. We show that gametogenesis proceeds asynchronously among oysters, even within the same sex individuals. Complex physiological trade-offs were discovered between sexes during gonad maturation; in early stages of sexual maturation, females prioritized cell division, whereas males suppressed it. Females exhibited higher expression of solute carrier family (SLC) genes, suggesting enhanced nutrient exchange during oogenesis. Temporal dynamics highlighted differential expression of genes regulating cross-membrane ion gradients (e.g., transient receptor potential channels) and signal transduction (e.g., signal transducer and activator of transcription), previously linked to environmental sex determination (ESD) in some reptilian species. Conclusions: Together, these findings underscore that gametogenesis in Pacific oysters is complex and dynamic, and that molecular pathways of ESD may be partially conserved between invertebrate and vertebrate species. Full article

The evolution of sixth-generation (6G) networks enables transformative edge Artificial Intelligence of Things (AIoT) applications but introduces critical security vulnerabilities during model transmission between the central server and edge devices (e.g., unmanned aerial vehicles). Traditional approaches fail to jointly optimize model accuracy and physical layer security against eavesdropping. To address this gap, we propose a novel dynamic user selection framework that integrates three key innovations: (1) closed-form secrecy outage probability derivation for Rayleigh fading channels, (2) a Secure Model Accuracy (SMA) metric unifying recognition accuracy and secrecy outage probability, and (3) an alternating optimization algorithm for joint model–bandwidth selection under secrecy constraints. Comprehensive simulations demonstrate 22% SMA gains over baselines across diverse channel conditions and eavesdropper capabilities, resolving the fundamental accuracy–security tradeoff for trustworthy edge intelligence. Full article

Plant disease segmentation has achieved significant progress with the help of artificial intelligence. However, deploying high-accuracy segmentation models in resource-limited settings faces three key challenges, as follows: (A) Traditional dense attention mechanisms incur quadratic computational complexity growth (O($n^{2}d$)), rendering them ill-suited for low-power hardware. (B) Naturally sparse spatial distributions and large-scale variations in the lesions on leaves necessitate models that concurrently capture long-range dependencies and local details. (C) Complex backgrounds and variable lighting in field images often induce segmentation errors. To address these challenges, we propose Sparse-MoE-SAM, an efficient framework based on an enhanced Segment Anything Model (SAM). This deep learning framework integrates sparse attention mechanisms with a two-stage mixture of experts (MoE) decoder. The sparse attention dynamically activates key channels aligned with lesion sparsity patterns, reducing self-attention complexity while preserving long-range context. Stage 1 of the MoE decoder performs coarse-grained boundary localization; Stage 2 achieves fine-grained segmentation by leveraging specialized experts within the MoE, significantly enhancing edge discrimination accuracy. The expert repository—comprising standard convolutions, dilated convolutions, and depthwise separable convolutions—dynamically routes features through optimized processing paths based on input texture and lesion morphology. This enables robust segmentation across diverse leaf textures and plant developmental stages. Further, we design a sparse attention-enhanced Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contexts for both extensive lesions and small spots. Evaluations on three heterogeneous datasets (PlantVillage Extended, CVPPP, and our self-collected field images) show that Sparse-MoE-SAM achieves a mean Intersection-over-Union (mIoU) of 94.2%—surpassing standard SAM by 2.5 percentage points—while reducing computational costs by 23.7% compared to the original SAM baseline. The model also demonstrates balanced performance across disease classes and enhanced hardware compatibility. Our work validates that integrating sparse attention with MoE mechanisms sustains accuracy while drastically lowering computational demands, enabling the scalable deployment of plant disease segmentation models on mobile and edge devices. Full article

The rapid advancement of Artificial Intelligence of Things (AIoT) has driven an urgent demand for intelligent video anomaly detection (VAD) to ensure industrial safety. However, traditional approaches struggle to detect unknown anomalies in complex and dynamic environments due to the scarcity of abnormal samples and limited generalization capabilities. To address these challenges, this paper presents an adaptive VAD framework powered by edge intelligence tailored for resource-constrained industrial settings. Specifically, a lightweight feature extractor is developed by integrating residual networks with channel attention mechanisms, achieving a 58% reduction in model parameters through dense connectivity and output pruning. A multidimensional evaluation strategy is introduced to dynamically select optimal models for deployment on heterogeneous edge devices. To enhance cross-scene adaptability, we propose a multilayer adversarial domain adaptation mechanism that effectively aligns feature distributions across diverse industrial environments. Extensive experiments on a real-world coal mine surveillance dataset demonstrate that the proposed framework achieves an accuracy of 86.7% with an inference latency of 23 ms per frame on edge hardware, improving both detection efficiency and transferability. Full article

Partial discharge (PD) is not only a critical indicator but also a major accelerating factor of insulation degradation in power transformers. Accurate identification of PD types is essential for diagnosing insulation defects and locating faults in transformers. Traditional methods based on phase-resolved partial discharge (PRPD) patterns typically rely on expert interpretation and manual feature extraction, which are increasingly being supplanted by Convolutional Neural Networks (CNNs) due to their ability to automatically extract features and deliver high classification accuracy. However, the inherent subtlety and diversity of characteristic differences among PRPD patterns, coupled with substantial noise resulting from complex electromagnetic interference, present significant hurdles to achieving accurate identification. This paper proposes a transformer partial discharge identification method based on Deep Residual Shrinkage Network (DRSN) to address these challenges. The method integrates dual-path feature extraction to capture both local and global features, incorporates a channel-domain adaptive soft-thresholding mechanism to effectively suppress noise interference, and utilizes the Focal Loss function to enhance the model’s attention to hard-to-classify samples. To validate the proposed method, given the scarcity of diverse real-world transformer PD data, an experimental platform was utilized to generate and collect PD data by artificially simulating various discharge defect models, including tip discharge, surface discharge, air-gap discharge and floating discharge. Data diversity was then enhanced through sample augmentation and noise simulation, to minimize the gap between experimental data and real-world on-site data. Experimental results demonstrate that the proposed method achieves superior partial discharge recognition accuracy and strong noise robustness on the experimental dataset. For future work, it is essential to collect more real transformer PD data to further validate and strengthen the model’s generalization capability, thereby ensuring its robust performance and applicability in practical scenarios. Full article

Deterministic random bit generators (DRBG) play a crucial role in device security because they generate secret information cryptographic systems, e.g., secret keys and parameters. Thus, attacks on DRBGs can result in the exposure of important secret values, which can threaten the entire cryptographic system of the target Internet of Things (IoT) equipment and smart devices. In 2020, Meyer proposed a side-channel attack (SCA) method that recovers the output random bits by analyzing the power consumption traces of the NIST standard AES CTR DRBG. In addition, most algorithmic countermeasures against SCAs also utilize random numbers; thus, such vulnerabilities are more critical than other SCAs on cryptographic modules. Meyer’s attack recovers the secret random number in four stages of the attack using only the power traces, which the CTR DRBG processes in 256 blocks. We present an approach that employs a clustering algorithm to enhance Meyer’s attack. The proposed attack increases the attack success rate and recovers more information using a clustering attack in the first step. In addition, it improves the attack accuracy in the third and fourth steps using the information obtained from the clustering process. These results lead to the possibility of attacks at higher noise levels and increase the diversity of target devices for attacking the CTR DRBG. Experiments were conducted on an Atmel XMEGA128D4 processor to evaluate the effectiveness of the proposed attack method. We also introduced artificial noise into the power traces to compare the proposed attack’s performance at different noise levels. Our results demonstrate that the first step of the proposed attack achieves a higher success rate than Meyer’s attack at all noise levels. For example, at high noise levels, the difference in the success rates is up to 50%. In steps 3 and 4, an average performance improvement of 18.5% greater than Meyer’s proposed method is obtained. The proposed attack effectively extends the target to more noisy environments than previous attacks, thereby increasing the threat of SCA on CTR DRBGs. Full article

Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation. Full article

The backbone of the Internet of Things ecosystem relies heavily on wireless sensor networks and low-power wide area network technologies, such as LoRa modulation, to provide the long-range, energy-efficient communications essential for applications as diverse as smart homes, healthcare, agriculture, smart grids, and transportation. With the number of IoT devices expected to reach approximately 41 billion by 2034, managing radio spectrum resources becomes a critical issue. However, as these devices are deployed at an increasing rate, the limited spectral resources will result in increased interference, packet collisions, and degraded quality of service. Current methods for increasing network capacity have limitations and require advanced solutions. This paper proposes a novel hybrid spectrum sensing framework that combines traditional signal processing and artificial intelligence techniques specifically designed for LoRa spreading factor detection and communication channel analytics. Our proposed framework processes wideband signals directly from IQ samples to identify and classify multiple concurrent LoRa transmissions. The results show that the framework is highly effective, achieving a detection accuracy of 96.2%, a precision of 99.16%, and a recall of 95.4%. The proposed framework’s flexible architecture separates the AI processing pipeline from the channel analytics pipeline, ensuring adaptability to various communication protocols beyond LoRa. Full article

Peru is one of the most diverse countries from a geographical and climatic point of view, where there are three large ecosystem regions called coast, Sierra, and jungle. These characteristics result in the country having many hydrographic basins, with rivers of significant dimensions in terms of the width and length of the channel. In this sense, there is a permanent need to provide connectivity and promote trade between communities through road bridge infrastructure. Thus, Peru historically developed a road network and bridges during the Inca Empire in the Tawantinsuyu region, building a cobblestone road network and suspension bridges with rope cables made of plant fibers from vegetation called Coya-Ichu. This is how bridges in Peru have evolved to meet contemporary vehicular demands and provide structural stability and functionality throughout their useful life. This article presents the following sections: (a) an introduction to the evolution of bridges, (b) the current typology and inventory of bridges, (c) the characterization of the largest bridges, (d) a discussion on the architecture, engineering, construction, and structural health monitoring (AECSHM) of bridges in the face of climate change, earthquakes, and material degradation, and (e) conclusions. Finally, this article presents opportunities and challenges in terms of Peru’s architecture, engineering, construction, and structural health monitoring of road bridges. Special emphasis is given to the use of technologies from the era of Industry 4.0 to promote the digital construction and structural health monitoring of these infrastructures. Finally, it is concluded that the integration of technologies of sensors, the IoT (Internet of Things), AI (artificial intelligence), UAVs (Unmanned Aerial Vehicles), remote sensing, BIM (Building Information Modeling), and DfMA (Design for Manufacturing and Assembly), among others, will allow for more safe, reliable, durable, productive, cost-effective, sustainable, and resilient bridge infrastructures in Peru in the face of climate change. Full article

There are multiple uses for single-channel images, such as infrared imagery, depth maps, and others. To automatically classify objects in such images, an algorithm suited for single-channel image processing is required. This study explores the application of deep learning techniques for the recognition of wild animals using infrared images. Traditional methods of wildlife monitoring often rely on visible light imaging, which can be hindered by various environmental factors such as darkness, fog, and dense foliage. In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. The model is trained and tested on a diverse dataset of infrared images, demonstrating high accuracy in distinguishing between multiple species. In this paper, we also present a comparison of several well-known artificial neural networks on this data. To ensure accurate testing, we introduce a new dataset containing infrared photos of Slovak wildlife, specifically including classes such as bear, deer, boar, and fox. To complement this dataset, the Fashion MNIST dataset was also used. Our results indicate that deep learning approaches significantly enhance the capability of infrared imaging for wildlife monitoring, offering a reliable and efficient tool for conservation efforts and ecological studies. Full article

Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer detection. Convolutional neural networks (CNNs) and support vector machines (SVMs) have been used in computer-aided diagnosis (CAD) systems to identify breast tumors from mammograms. However, existing methods often face challenges in accuracy and reliability across diverse diagnostic scenarios. This paper proposes a three parallel channel artificial intelligence-based system. First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. Third, a newly developed CNN is trained and used to classify mammogram images. Finally, a decision fusion that combines results from the three channels to enhance system performance is implemented using different rules. The proposed decision fusion-based system outperforms state-of-the-art alternatives with an overall accuracy of 99.1% using the product rule. Full article