Search Results (2,881)

This paper introduces a method for estimating the Downlink Angle of Departure (DL-AoD) of 5G User Equipment (UE) from measured signal strengths of directionally transmitted synchronization signals. Based on estimated DL-AoD values, from two or more anchor nodes, the position of the UE was estimated. Unlike most prior work, which is simulation-based or relies on custom testbeds, this study uses real measurements from an operational 5G network in an industrial factory environment. A deterministic estimator was derived, but multipath and unknown beam characteristics limit its accuracy. To address this, machine learning was applied to automatically adapt to the environment. Previous simulation studies reported 90th-percentile DL-AoD estimation errors below 2°, while experimental works achieved best-case accuracies of 5–6°. In this study, the experimental DL-AoD estimation error remained below 4° for 90% of the measurements, indicating improved real-world performance. Reported positioning errors in the literature range from 3.8 m to 140 m, whereas the 13.2 m error obtained here lies near the midpoint of this range, confirming the practicality of the proposed method in industrial environments. Compared to existing approaches, this work demonstrates high angular accuracy using only sub-6 GHz beams in a realistic industrial scenario without detailed knowledge of antenna beam patterns and channel state. The findings demonstrate that standard 5G signals can provide accurate indoor localization without additional infrastructure, offering a practical path toward cost-effective positioning in industrial IoT and automation. Full article

Background/Objectives: The present study investigates the influence of advanced windowing techniques and the combination of different classification methods on the accuracy of dens axis fracture detection in computed tomography (CT) images. The aim was to evaluate and compare the diagnostic performance of two different computational models—a pure deep learning (DL) approach and a combined approach of DL segmentation, windowing, and radiomics. Methods: In this retrospective study, CT datasets of the upper cervical spine of 366 patients were included. All datasets were further divided into training, validation, and test sets. Model 1 (M1) relied on a pure DL method using a Convolutional Neural Network (CNN) and a Feedforward Neural Network (FNN), without prior manual segmentation. Model 2 (M2) incorporated a fully automatic U-Net-based segmentation followed by radiomics feature extraction and classification using a Machine Learning (ML) Classifier. The performance of both models was measured by classification accuracy, with a particular focus on the impact of CT windowing parameters and the chosen ML classification strategies. Results: M1 achieved a maximum classification accuracy of 93.7%, while M2 accomplished a classification accuracy of up to 95.7% by using ROI-based windowing and advanced feature extraction. Conclusions: Integrating advanced windowing techniques, U-Net segmentation, and radiomics improves the detection of dens axis fractures in CT imaging. This approach could enhance diagnostic accuracy and warrants further exploration and clinical integration. Full article

Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality agricultural development and ecological balance, this study took coastal saline–alkali land as a case study. It adopted the extreme gradient boosting (XGB) model optimized by the tree-structured Parzen estimator (TPE) algorithm, combined with in situ hyperspectral (ISH) and spaceborne hyperspectral (SBH) data, to predict and map soil organic matter and four soil nutrients: alkali nitrogen (AN), available phosphorus (AP), and available potassium (AK). From the research outputs, one can deduce that superior predictive efficacy is exhibited by the TPE-XGB construct, employing in situ hyperspectral datasets. Among these, available phosphorus (R² = 0.67) exhibits the highest prediction accuracy, followed by organic matter (R² = 0.65), alkali-hydrolyzable nitrogen (R² = 0.56), and available potassium (R² = 0.51). In addition, the spatial continuity mapping results based on spaceborne hyperspectral data show that SOM, AN, AP, and AK in soil nutrients in the study area are concentrated in the northern, eastern, southern, and riverbank and estuarine delta areas, respectively. The variability of soil nutrients from large to small is phosphorus, potassium, nitrogen, and organic matter. The SHAP (SHapley Additive exPlanations) analysis results reveal that the bands with the greatest contribution to the fitting of SOM, AN, AP, and AK are 612 nm, 571 nm, 1493 nm, and 1308 nm, respectively. Extending into realms of hierarchical partitioning (HP) and variation partitioning (VP), it is discerned that climatic factors (CLI) alongside vegetative aspects (VEG) wield dominant influence upon the spatial differentiation manifest in nutrients. Meanwhile, comparatively diminished are the contributions possessed by terrain (TER) and soil property (SOIL). In summary, this study effectively assessed the significant variation patterns of soil nutrient distribution in coastal saline–alkali soils using the TPE-XGB model, providing scientific basis for the sustainable advancement of agricultural development in saline–alkali coastal regions. Full article

Hollow heart, and other crop defects, can be devastating to farmers. Hollow heart is not a disease but a physiological disorder affected by temperature, soil moisture, plant density, and other factors. These defects can cause substantial annual losses for farmers. Currently, potatoes are shipped and inspected from producers to shipping points and markets. At these facilities, samples are inspected for defects. Detection of hollow heart consists of halving potatoes and visually inspecting for defects. The defect size is compared to USDA hollow heart classification charts for acceptance or rejection. An automatic, non-destructive system to identify hollow heart has the potential to improve quality. Two methods have been developed to collect data for such a system: acoustic signal capture and visual/vibration signal capture. Data is collected and stored for one potato at a time. The procedure includes the collection of weight, proportional size, and volume, as well as the generation of an acoustic sound signal through a drop test and a motion signal captured through a vision system. To simulate hollow heart, potatoes are cored and retested by producing a new set of data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1000 samples, each comprising proportional volume, weight, proportional size, motion, and acoustic data. Such a dataset does not exist in the current literature and can serve for the development of machine learning algorithms to detect hollow heart nondestructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning. Full article

Breast cancer is the most commonly diagnosed cancer in women globally and represents the leading cause of mortality related to malignant tumors. Currently, healthcare professionals are focused on developing and implementing innovative techniques to improve the early detection of this disease. Thermography, studied as a complementary method to traditional approaches, captures infrared radiation emitted by tissues and converts it into data about skin surface temperature. During tumor development, angiogenesis occurs, increasing blood flow to support tumor growth, which raises the surface temperature in the affected area. Automatic classification techniques have been explored to analyze thermographic images and develop an optimal classification tool to identify thermal anomalies. This study aims to design a concise description using statistical and texture features to accurately classify thermograms as control or highly probable to be cancer (with thermal anomalies). The importance of employing a short description lies in facilitating interpretation by medical professionals. In contrast, a characterization based on a large number of variables could make it more challenging to identify which values differentiate the thermograms between groups, thereby complicating the explanation of results to patients. A maximum accuracy of 91.97% was achieved by applying only seven features and using a Coarse Decision Tree (DT) classifier and robust Machine Learning (ML) model, which demonstrated competitive performance compared with previously reported studies. Full article

Hair loss is a common issue that influences many people around the world and can lead to mental and social challenges, which can bring down self-esteem and social relationships. To overcome these challenges, this study investigates the promising role of machine learning (ML) in the early detection and determination of hair loss, clearing the way for personalized medicines. In order to arrive at a particular outcome, the research incorporates a few techniques, including Random Forest, Support Vector Machines (SVMs), as well as K-nearest neighbor (KNN). Important elements like feature engineering, preprocessing, and hyperparameter tweaking are used. Traditional approaches are outrun by the outcomes reached, and there is a clear difference when it comes to the accuracy and precision. This study shows the potential of automatic diagnostics that could transform the treatment of hair loss to the enormous benefit of the many afflicted by it. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

With the rapid development of artificial intelligence and machine learning technologies, automatic speech recognition (ASR) and text-to-speech (TTS) have become key components of the digital transformation of society. The Kazakh language, as a representative of the Turkic language family, remains a low-resource language with limited audio corpora, language models, and high-quality speech synthesis systems. This study provides a comprehensive analysis of existing speech recognition and synthesis models, emphasizing their applicability and adaptation to the Kazakh language. Special attention is given to linguistic and technical barriers, including the agglutinative structure, rich vowel system, and phonemic variability. Both open-source and commercial solutions were evaluated, including Whisper, GPT-4 Transcribe, ElevenLabs, OpenAI TTS, Voiser, KazakhTTS2, and TurkicTTS. Speech recognition systems were assessed using BLEU, WER, TER, chrF, and COMET, while speech synthesis was evaluated with MCD, PESQ, STOI, and DNSMOS, thus covering both lexical–semantic and acoustic–perceptual characteristics. The results demonstrate that, for speech-to-text (STT), the strongest performance was achieved by Soyle on domain-specific data (BLEU 74.93, WER 18.61), while Voiser showed balanced accuracy (WER 40.65–37.11, chrF 80.88–84.51) and GPT-4 Transcribe achieved robust semantic preservation (COMET up to 1.02). In contrast, Whisper performed weakest (WER 77.10, BLEU 13.22), requiring further adaptation for Kazakh. For text-to-speech (TTS), KazakhTTS2 delivered the most natural perceptual quality (DNSMOS 8.79–8.96), while OpenAI TTS achieved the best spectral accuracy (MCD 123.44–117.11, PESQ 1.14). TurkicTTS offered reliable intelligibility (STOI 0.15, PESQ 1.16), and ElevenLabs produced natural but less spectrally accurate speech. Full article

Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 850 hPa, specifically designed to generate physically consistent labels for machine learning applications. The approach combines the Thermal Front Parameter (TFP) with temperature advection (AdvT), applying optimized thresholds (TFP < 5 × 10⁻¹¹ K m⁻²; AdvT < −1 × 10⁻⁴ K s⁻¹), morphological filtering, and polynomial smoothing. Comparison against 1426 manual charts from 2009 revealed systematic spatial displacement, with mean offsets of ~502 km. Although pixel-level overlap was low, with Intersection over Union (IoU) = 0.013 and Dice coefficient (Dice) = 0.034, spatial concordance exceeded 99%, confirming both methods identify the same synoptic systems. The automatic method detects 58% more fronts over the South Atlantic and 44% fewer over the Andes compared to manual charts. Seasonal variability shows maximum activity in austral winter (31.3%) and minimum in summer (20.1%). This is the first automatic front detection system calibrated for South America that maintains direct correspondence between training labels and reanalysis input fields, addressing the spatial misalignment problem that limits deep learning applications in atmospheric sciences. Full article

This research introduces a system designed to predict three-dimensional airspace occupancy over Colombia using historical Automatic Dependent Surveillance-Broadcast (ADS-B) data and machine learning techniques. The goal is to support proactive air traffic management by estimating future aircraft positions—specifically their latitude, longitude, and flight level. To achieve this, four predictive models were developed and tested: K-Nearest Neighbors (KNN), Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Among them, the LSTM model delivered the most accurate results, with a Mean Absolute Error (MAE) of 312.59, a Root Mean Squared Error (RMSE) of 1187.43, and a coefficient of determination (R²) of 0.7523. Compared to the baseline models (KNN, Random Forest, XGBoost), these values represent an improvement of approximately 91% in MAE, 83% in RMSE, and an eighteen-fold increase in R², demonstrating the substantial advantage of the LSTM approach. These metrics indicate a significant improvement over the other models, particularly in capturing temporal patterns and adjusting to evolving traffic conditions. The strength of the LSTM approach lies in its ability to model sequential data and adapt to dynamic environments—making it especially suitable for supporting future Trajectory-Based Operations (TBO). The results confirm that predicting airspace occupancy in three dimensions using historical data are not only possible but can yield reliable and actionable insights. Looking ahead, the integration of hybrid neural network architectures and their deployment in real-time systems offer promising directions to enhance both accuracy and operational value. Full article

Recent developments in next-generation sequencing technology have led to the creation of extensive, open-source protein databases consisting of hundreds of millions of sequences. To render these sequences applicable in biomedical applications, they must be meticulously annotated by wet lab testing or extracting them from existing literature. Over the last few years, researchers have developed numerous automatic annotation systems, particularly deep learning models based on machine learning and artificial intelligence, to address this issue. In this work, we propose a transformer-based fusion model capable of predicting Gene Ontology (GO) terms from full-scale protein sequences, achieving state-of-the-art accuracy compared to other contemporary machine learning annotation systems. The approach performs particularly well on clustered split datasets, which comprise training and testing samples originating from distinct distributions that are structurally diverse. This demonstrates that the model is able to understand both short and long term dependencies within the protein’s structure and can capture sequence features that are predictive of the various GO terms. Furthermore, the technique is lightweight and less computationally expensive compared to the benchmark methods, while at the same time unaffected by sequence length, rendering it appropriate for diverse applications with varying sequence lengths. Full article

This paper presents an automatic system for the classification of musical instruments from audio recordings. The project leverages deep learning (DL) techniques to achieve its objective, exploring three different classification approaches based on distinct input representations. The first method involves the extraction of Mel-Frequency Cepstral Coefficients (MFCCs) from the audio files, which are then fed into a two-dimensional convolutional neural network (Conv2D). The second approach makes use of mel-spectrogram images as input to a similar Conv2D architecture. The third approach employs conventional machine learning (ML) classifiers, including Logistic Regression, K-Nearest Neighbors, and Random Forest, trained on MFCC-derived feature vectors. To gain insight into the behavior of the DL model, explainability techniques were applied to the Conv2D model using mel-spectrograms, allowing for a better understanding of how the network interprets relevant features for classification. Additionally, t-distributed stochastic neighbor embedding (t-SNE) was employed on the MFCC vectors to visualize how instrument classes are organized in the feature space. One of the main challenges encountered was the class imbalance within the dataset, which was addressed by assigning class-specific weights during training. The results, in terms of classification accuracy, were very satisfactory across all approaches, with the convolutional models and Random Forest achieving around 97–98%, and Logistic Regression yielding slightly lower performance. In conclusion, the proposed methods proved effective for the selected dataset, and future work may focus on further improving class balance techniques. Full article

This work aims towards the automatic detection of faults in industrial air-cooling equipment used in a production line for staple fibers and ultimately provides maintenance scheduling recommendations to ensure seamless operation. In this context, various deep learning models are tested to ultimately define the most effective one for the intended scope. In the examined system, four vibration and temperature sensors are used, each positioned radially on the motor body near the rolling bearing of the motor shaft—a typical setup in many industrial environments. Thus, by collecting and using data from the latter sources, this work exhaustively investigates the feasibility of accurately diagnosing faults in staple fiber cooling fans. The dataset is acquired and constructed under real production conditions, including variations in rotational speed, motor load, and three fault priorities, depending on the model detection accuracy, product specification, and maintenance requirements. Fault identification for training purposes involves analyzing and evaluating daily maintenance logs for this equipment. Experimental evaluation on real production data demonstrated that the proposed ResNet50-1D model achieved the highest overall classification accuracy of 97.77%, while effectively resolving the persistent misclassification of the faulty impeller observed in all the other models. Complementary evaluation confirmed its robustness, cross-machine generalization, and suitability for practical deployment, while the integration of predictions with maintenance logs enables a severity-based prioritization strategy that supports actionable maintenance planning. Full article

Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network. Full article

With the advancement of intelligent technology and the rise in labor costs, manual identification and cutting of 3D cotton cup indentations can no longer meet modern demands. The increasing variety and shape of 3D cotton cups due to personalized requirements make the use of fixed molds for cutting inefficient, leading to a large number of molds and high costs. Therefore, this paper proposes a UNet-based indentation segmentation algorithm to automatically extract 3D cotton cup indentation data. By incorporating the VGG16 network and Leaky-ReLU activation function into the UNet model, the method improves the model’s generalization capability, convergence speed, detection speed, and reduces the risk of overfitting. Additionally, attention mechanisms and an Atrous Spatial Pyramid Pooling (ASPP) module are introduced to enhance feature extraction, improving the network’s spatial feature extraction ability. Experiments conducted on a self-made 3D cotton cup dataset demonstrate a precision of 99.53%, a recall of 99.69%, a mIoU of 99.18%, and an mPA of 99.73%, meeting practical application requirements. The extracted 3D cotton cup indentation contour data is automatically input into an intelligent CNC cutting machine to cut 3D cotton cup. The cutting results of 400 data points show an 0.20 mm ± 0.42 mm error, meeting the cutting accuracy requirements for flexible material 3D cotton cups. This study may serve as a reference for machine vision, image segmentation, improvements to deep learning architectures, and automated cutting machinery for flexible materials such as fabrics. Full article