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With the rapid advancement of urban intelligence, there is an increasingly urgent demand for technological innovation in traffic management. License plate recognition technology can achieve high accuracy under ideal conditions but faces significant challenges in complex traffic environments and adverse weather conditions. To address these challenges, we propose the enhanced attention network and decoding for license plate recognition model (EAND-LPRM). This model leverages an encoder to extract features from image sequences and employs a self-attention mechanism to focus on critical feature information, enhancing its capability to handle complex traffic scenarios such as rainy weather and license plate distortion. We have curated and utilized publicly available datasets that closely reflect real-world scenarios, ensuring transparency and reproducibility. Experimental evaluations conducted on these datasets, which include various complex scenarios, demonstrate that the EAND-LPRM model achieves an accuracy of 94%, representing a 6% improvement over traditional license plate recognition algorithms. The main contributions of this research include the development of a novel attention-mechanism-based architecture, comprehensive evaluation on multiple datasets, and substantial performance improvements under diverse and challenging conditions. This study provides a practical solution for automatic license plate recognition systems in dynamic and unpredictable environments. [ABSTRACT FROM AUTHOR]

Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system for real-time fall detection is proposed, using the ASSIST-IoT reference architecture. Empowered with a machine learning model, the system can detect fall accidents and swiftly notify the occupational health and safety manager. To train the model, a novel multimodal fall detection dataset was collected from ten human participants and an anthropomorphic dummy, covering multiple types of fall, including falls from a height. The dataset includes absolute location and acceleration measurements from several IoT devices. Furthermore, a lightweight long short-term memory model is proposed for fall detection, capable of operating in an IoT environment with limited network bandwidth and hardware resources. The accuracy and F1-score of the model on the collected dataset were shown to exceed 0.95 and 0.9, respectively. The collected multimodal dataset was published under an open license, to facilitate future research on fall detection methods in occupational health and safety. [ABSTRACT FROM AUTHOR]

This work analyzes a new indoor laboratory dataset looking at early fire indicators in controlled and realistic experiments representing different incipient fire scenarios. The experiments were performed within the constraints of an indoor laboratory setting using multiple distributed sensor nodes in different room positions. Each sensor node collected data of particulate matter (PM), volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO 2 ), hydrogen (H 2 ), ultraviolet radiation (UV), air temperature, and humidity in terms of a multivariate time series. These data hold immense value for researchers within the machine learning and data science communities who are keen to explore innovative and advanced statistical and machine learning techniques. They serve as a valuable resource for the development of early fire detection systems. The analysis of the collected data was carried out depending on the Manhattan distance between the fire source and the sensor node. We found that especially larger particles (>0.5 μ m) and VOCs show a significant dependency with respect to the intensity as a function of the Manhattan distance to the source. Moreover, we observed differences in the propagation behavior of VOCs, PM, and CO, which are particularly relevant in incipient fire scenarios due to the presence of strand propagation effects. [ABSTRACT FROM AUTHOR]

The thalamus is a subcortical brain structure linked to the motor system. Since certain changes within this structure are related to diseases, such as multiple sclerosis and Parkinson's, the characterization of the thalamus—e.g., shape assessment—is a crucial step in relevant studies and applications, including medical research and surgical planning. A robust and reliable thalamus-segmentation method is therefore, required to meet these demands. Despite presenting low contrast for this particular structure, T1-weighted imaging is still the most common MRI sequence for thalamus segmentation. However, diffusion MRI (dMRI) captures different micro-structural details of the biological tissue and reveals more contrast of the thalamic borders, thereby serving as a better candidate for thalamus-segmentation methods. Accordingly, we propose a baseline multimodality thalamus-segmentation pipeline that combines dMRI and T1-weighted images within a CNN approach, achieving state-of-the-art levels of Dice overlap. Furthermore, we are hosting an open benchmark with a large, preprocessed, publicly available dataset that includes co-registered, T1-weighted, dMRI, manual thalamic masks; masks generated by three distinct automated methods; and a STAPLE consensus of the masks. The dataset, code, environment, and instructions for the benchmark leaderboard can be found on our GitHub and CodaLab. [ABSTRACT FROM AUTHOR]

Measuring and adjusting the training load is essential in resistance training, as training overload can increase the risk of injuries. At the same time, too little load does not deliver the desired training effects. Usually, external load is quantified using objective measurements, such as lifted weight distributed across sets and repetitions per exercise. Internal training load is usually assessed using questionnaires or ratings of perceived exertion (RPE). A standard RPE scale is the Borg scale, which ranges from 6 (no exertion) to 20 (the highest exertion ever experienced). Researchers have investigated predicting RPE for different sports using sensor modalities and machine learning methods, such as Support Vector Regression or Random Forests. This paper presents PERSIST, a novel dataset for predicting PERceived exertion during reSIStance Training. We recorded multiple sensor modalities simultaneously, including inertial measurement units (IMU), electrocardiography (ECG), and motion capture (MoCap). The MoCap data has been synchronized to the IMU and ECG data. We also provide heart rate variability (HRV) parameters obtained from the ECG signal. Our dataset contains data from twelve young and healthy male participants with at least one year of resistance training experience. Subjects performed twelve sets of squats on a Flywheel platform with twelve repetitions per set. After each set, subjects reported their current RPE. We chose the squat exercise as it involves the largest muscle group. This paper demonstrates how to access the dataset. We further present an exploratory data analysis and show how researchers can use IMU and ECG data to predict perceived exertion. [ABSTRACT FROM AUTHOR]

Concealed objects detection in terahertz imaging is an urgent need for public security and counter-terrorism. So far, there is no public terahertz imaging dataset for the evaluation of objects detection algorithms. This paper provides a public dataset for evaluating multi-object detection algorithms in active terahertz imaging. Due to high sample similarity and poor imaging quality, object detection on this dataset is much more difficult than on those commonly used public object detection datasets in the computer vision field. Since the traditional hard example mining approach is designed based on the two-stage detector and cannot be directly applied to the one-stage detector, this paper designs an image-based Hard Example Mining (HEM) scheme based on RetinaNet. Several state-of-the-art detectors, including YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet, are evaluated on this dataset. Experimental results show that the RetinaNet achieves the best mAP and HEM further enhances the performance of the model. The parameters affecting the detection metrics of individual images are summarized and analyzed in the experiments. [ABSTRACT FROM AUTHOR]

Featured Application: This dataset can be used for supervised training of models for colorectal polyp detection, localisation, segmentation and classification. Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes. [ABSTRACT FROM AUTHOR]

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera's video signal and achieved high inter-rater agreement (Fleiss' Kappa = 0.96 ). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ± 0.18 ), recall of 0.85 ± 0.13 , and precision of 0.79 ± 0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living. [ABSTRACT FROM AUTHOR]

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research. [ABSTRACT FROM AUTHOR]

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review. [ABSTRACT FROM AUTHOR]

In this study, pre-impact fall detection algorithms were developed based on data gathered by a custom-made inertial measurement unit (IMU). Four types of simulated falls were performed by 40 healthy subjects (age: 23.4 ± 4.4 years). The IMU recorded acceleration and angular velocity during all activities. Acceleration, angular velocity, and trunk inclination thresholds were set to 0.9 g, 47.3°/s, and 24.7°, respectively, for a pre-impact fall detection algorithm using vertical angles (VA algorithm); and 0.9 g, 47.3°/s, and 0.19, respectively, for an algorithm using the triangle feature (TF algorithm). The algorithms were validated by the results of a blind test using four types of simulated falls and six types of activities of daily living (ADL). VA and TF algorithms resulted in lead times of 401 ± 46.9 ms and 427 ± 45.9 ms, respectively. Both algorithms were able to detect falls with 100% accuracy. The performance of the algorithms was evaluated using a public dataset. Both algorithms detected every fall in the SisFall dataset with 100% sensitivity). The VA algorithm had a specificity of 78.3%, and TF algorithm had a specificity of 83.9%. The algorithms had higher specificity when interpreting data from elderly subjects. This study showed that algorithms using angles could more accurately detect falls. Public datasets are needed to improve the accuracy of the algorithms. [ABSTRACT FROM AUTHOR]