In recent years, the evolution of technology has proven essential in the progress of agriculture mechanization towards sustainable approaches. The introduction of the Unmanned Aircraft System (UAS) is continuously gro...
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The COVID-19 pandemic triggered a concerning rise in violence against women and children, known as The Shadow Pandemic. To address this, a Canadian foundation introduced the “Signal for Help” gesture to discreetly a...
The COVID-19 pandemic triggered a concerning rise in violence against women and children, known as The Shadow Pandemic. To address this, a Canadian foundation introduced the “Signal for Help” gesture to discreetly alert others in danger. However, the effectiveness of this approach depends on individuals recognizing and responding to the signal. In this paper, we propose an innovative solution that adopts the technology available in smart cities to detect the “Signal for Help” in real-time through surveillance footage. We developed and implemented a recognition algorithm on an affordable device that achieves accurate detection of the signal in 94 % of cases. This approach has the potential to improve the response to instances of violence, providing a reliable means of alerting authorities and support networks.
In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilitie...
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Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and ...
Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information on several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus, given the recent advances of machine learning, computer vision and the availability of labeled data, researchers demonstrated the feasibility and the precision of land-use monitoring systems and remote sensing image classification through the use of deep neural networks. Such systems may help domain experts and governments in constant environmental monitoring, enabling timely intervention in case of catastrophic events (e.g., forest wildfire in a remote area). Despite the recent advances in the field of computer vision, many works limit their analysis on Convolutional Neural Networks (CNNs) and, more recently, to vision transformers (ViTs). Given the recent successes of Graph Neural Networks (GNNs) on non-graph data, such as time-series and images, we investigate the performances of a recent Vision GNN architecture (ViG) applied to the task of land cover classification. The experimental results show that ViG achieves state-of-the-art performances in multiclass and multilabel classification contexts, surpassing both ViT and ResNet on large-scale benchmarks.
In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explore...
In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under the influence of molecular noise. Through two case studies, we demonstrate RL’s capabilities in navigating biological systems’ inherent non-linearity and high dimensionality. Our findings reveal that RL effectively identifies the onset of Hopf bifurcations and preserves biological plausibility within the optimized networks. However, challenges were encountered in achieving persistent oscillations and matching traditional algorithms’ computational speed. Despite these limitations, the study highlights RL’s significant potential as an instrumental tool in computational biology, offering a novel perspective for exploring and optimizing oscillatory dynamics within complex biological systems. Our research establishes RL as a promising strategy for manipulating and designing intricate behaviors in biological networks.
In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays...
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In recent years, the hyperspectral image (HSI) classification has attracted great attention in the field of earth observation. With the expansion of application scenarios and the continuous improvement of application ...
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We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in c...
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Voltage source inverters are widely used in industry due to their high control performance and efficiency in electric motor drive systems. The output voltage of inverter at the switching instance causes the generation...
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Smart glove is used to help deaf to interpret their communication. This tools is an interesting topic to be explored because its need improvement for the feature that user friendly and easy to used. In this paper, fle...
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