With the rapid growth of global air traffic, 4D-flight trajectory prediction (4D-FTP) using deep learning (DL) methods has become essential for applications such as flight delay prediction, fuel consumption analysis, ...
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In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorit...
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An algorithm for processing the continuous stream of incoming video data to detect and recognize signed expressions is proposed. The aim is to improve an assistive technology system. The method is based on the dynamic...
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An algorithm for processing the continuous stream of incoming video data to detect and recognize signed expressions is proposed. The aim is to improve an assistive technology system. The method is based on the dynamic flow of expression models between a repository of inactive models and pools of active and completed models. When modeling signed expressions, so-called visemes are used. These are units smaller than words - the equivalent of phonemes known from speech recognition. Visemes are extracted by clustering descriptors of the holistic skeletons. The sequential-simultaneous nature of gestures is considered, and the modeling is carried out in four parallel channels related to the position and shape of both hands. The conditions for activation, deactivation, and recognition of the model as completed are formulated, assuming asynchronous changes in individual channels. The method is successfully tested on a demanding dataset recorded by potential users.
This paper studies a class of perimeter defense problems for large-scale multi-robot systems. A perimeter defense strategy is designed via relative time calculations and maximum matching methods. The strategy is simpl...
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Image captioning represents a significant challenge within the field of computer Vision. This task involves processing an image as input, identifying objects within it, comprehending the relationships between these ob...
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Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumo...
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As digital technologies continue to advance, modern communication networks face unprecedented challenges in handling the vast amounts of data produced daily by connected intelligent devices. Autonomous vehicles, smart...
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As digital technologies continue to advance, modern communication networks face unprecedented challenges in handling the vast amounts of data produced daily by connected intelligent devices. Autonomous vehicles, smart sensors, IoT systems etc., are gaining more and more interest and new communication paradigms are needed. This thesis addresses these challenges by combining semantic communication with generative models to optimize image compression and resource allocation in edge networks. Unlike traditional bit-centric communication systems, semantic communication prioritizes the transmission of meaningful data specifically selected to convey the meaning rather than obtain a faithful representation of the original data. The communication infrastructure can benefit of the focus solely on the relevant parts of the data due to significant improvements in bandwidth efficiency and latency reduction. Central to this work is the design of semantic-preserving image compression algorithms, utilizing advanced generative models such as Generative Adversarial Networks and Denoising Diffusion Probabilistic Models. These algorithms compress images by encoding only semantically relevant features and exploiting the generative power at the receiver side. This allows for the accurate reconstruction of high-quality images with minimal data transmission. The thesis also introduces a Goal-Oriented edge network optimization framework based on the Information Bottleneck problem and stochastic optimization, ensuring that communication resources are dynamically allocated to maximize efficiency and task performance. By integrating semantic communication into edge networks, the proposed system achieves a balance between computational efficiency and communication effectiveness, making it particularly suited for real-time applications. The thesis compares the performance of these semantic communication models with conventional image compression techniques, using both classical and semantic-awar
With the progress of power grid technology and intelligent technology, intelligent inspection robot (IR) came into being and are expected to become the main force of substation inspection in the future. Among them, mo...
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Sticking valves tend to cause cycles in control systems used in industry, degrading product quality and yield. Many attempts have been made to alleviate the impact of stiction. Mechanical knockers are used with succes...
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Sticking valves tend to cause cycles in control systems used in industry, degrading product quality and yield. Many attempts have been made to alleviate the impact of stiction. Mechanical knockers are used with success to knock loose the sticking components. Most other stiction compensation methods attempt to find ways to move the control output by an amount greater than the stiction band, while still getting the valve position as close as possible to the desired position. This paper shows how, instead of overcoming stiction and getting the valve position to the control output, the valve can be moved such that over time, the valve is on average at the correct position, while still moving the valve in increments that are larger than the stiction band.
Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also ente...
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Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.
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