We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic...
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We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic point removal, multi-session map alignment, map change detection and map version control. First, our sensor-setup agnostic dynamic point removal algorithm works seamlessly with both hand-held and robot-mounted setups to produce clean static 3D maps. Second, the multi-session map alignment aligns these clean static maps automatically, without manual parameter fine-tuning, into a single reference frame, using a two stage approach based on feature descriptor matching and fine registration. Third, our novel map change detection identifies positive and negative changes between two aligned maps. Finally, the map version control maintains a single base map that represents the current state of the environment, and stores the detected positive and negative changes, and boundary information. Our unique map version control system can reconstruct any of the previous clean session maps and allows users to query changes between any two random mapping sessions, all without storing any input raw session maps, making it very unique. Extensive experiments are performed using hand-held commercial LiDAR mapping devices and open-source robot-mounted LiDAR SLAM algorithms to evaluate each module and the whole 3D lifelong mapping framework.
We introduce a population-based approach to solving parameterized graph problems for which the goal is to identify a small set of vertices subject to a feasibility criterion. The idea is to evolve a population of indi...
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ISBN:
(纸本)9783031700705;9783031700712
We introduce a population-based approach to solving parameterized graph problems for which the goal is to identify a small set of vertices subject to a feasibility criterion. The idea is to evolve a population of individuals where each individual corresponds to an optimal solution to a subgraph of the original problem. The crossover operation then combines both solutions and subgraphs with the hope to generate an optimal solution for a slightly larger graph. In order to correctly combine solutions and subgraphs, we propose a new crossover operator called generalized allelic crossover which generalizes uniform crossover by associating a probability at each locus depending on the combined alleles of the parents. We prove for graphs with n vertices and m edges, the approach solves the k-vertex cover problem in expected time O(4(k)m + m(4) log n) using a simple RLS-style mutation. This bound can be improved to O(4(k)m + m(2)nk log n) by using standard mutation constrained to the vertices of the graph.
We present a transceiver design for unsourced random access where users confront frequency-selective channels. We consider that transmitters use Tensor-Based Modulation (TBM) as a modulation scheme, i.e., their messag...
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ISBN:
(纸本)9789464593617;9798331519773
We present a transceiver design for unsourced random access where users confront frequency-selective channels. We consider that transmitters use Tensor-Based Modulation (TBM) as a modulation scheme, i.e., their messages are mapped onto rank-one tensors. When wireless channels are modeled as flat channels, a canonical polyadic decomposition (CPD) can efficiently perform user separation. The frequency selectivity assumption breaks the CPD structure. We introduce a tensor decomposition, considering this assumption with a minimum knowledge of the specific channel conditions. We show through numerical evaluations that this receiver design leads to better decoding performance in almost the same order of complexity as the state-of-the-art CPD-based receiver.
Diffusion Probabilistic Models (DPMs) have been recently utilized to deal with various blind image restoration (IR) tasks, where they have demonstrated outstanding performance in terms of perceptual quality. However, ...
Street monitoring can be used as an excellent tool to decrease the number of incidents by, for example, giving information to street users (pedestrians, vehicles, cyclists, etc.) about the position of other street use...
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ISBN:
(纸本)9783031456473;9783031456480
Street monitoring can be used as an excellent tool to decrease the number of incidents by, for example, giving information to street users (pedestrians, vehicles, cyclists, etc.) about the position of other street users and therefore helping prevent any possible harmful situation. Today, with the growing concern about privacy and data protection issues, the use of video and audio has become problematic in terms of street monitoring, so there is a need to find a solution to this problem. With that in mind, using radar is a possible solution since the data retrieved from it doesn't contain anything considered personal and could violate people's privacy. This paper presents a systematic review of pedestrian, vehicle and cyclist detection. The objective is to identify the main methods of radar target detection and the algorithms. With that in mind, a search in the SCOPUS repository identified thirteen papers as relevant to include in the review.
Deep neural networks have been used to solve Ising models, including autoregressive neural networks, convolutional neural networks, recurrent neural networks, and graph neural networks. Learning probability distributi...
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Deep neural networks have been used to solve Ising models, including autoregressive neural networks, convolutional neural networks, recurrent neural networks, and graph neural networks. Learning probability distributions of energy configuration or finding ground states of disordered, fully connected Ising models is essential for statistical mechanics and NP-hard problems. Despite tremendous efforts, neural network architectures with abilities to high-accurately solve these intractable problems on larger systems remain a challenge. Here we propose a variational autoregressive architecture with a message passing mechanism, which effectively utilizes the interactions between spin variables. The architecture trained under an annealing framework outperforms existing neural network-based methods in solving several prototypical Ising spin Hamiltonians, especially for larger systems at low temperatures. The advantages also come from the great mitigation of mode collapse during training process. Considering these difficult problems to be solved, our method extends computational limits of unsupervised neural networks to solve combinatorial optimization problems. Ising problems are ubiquitous in physics, hence the exploration of new methods to tackle Ising problems with large scale and dense connectivity is of great interest. The authors propose a variational autoregressive architecture with a message passing mechanism that uses the interactions between spin variables, while previous methods build on the correlations only.
Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives by enabling a high degree of automation, through its...
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Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low-latency communications. In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorize and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green wireless networks. Then, as a main contribution, we survey in detail -from a theoretical and a practical viewpoint- the main energy efficiency enabling technologies that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key enabling technologies, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of -and the status on- green wireless networking. Finally, the areas of research where more effort is needed to make future wireless networks greener are also discussed.
Picking up objects and tossing them on a conveyor belt are activities generated on a daily basis in industry. These tasks are still done largely by humans. This article proposes a unified motion generator for a bimanu...
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Picking up objects and tossing them on a conveyor belt are activities generated on a daily basis in industry. These tasks are still done largely by humans. This article proposes a unified motion generator for a bimanual robotic system that enables two seven-degree-of-freedom robotic arms to grab and toss an object in one swipe. Unlike classical approaches that grab the object with quasi-zero contact velocity, the proposed approach is able to grasp the object while in motion. We control the contact forces prior to and following impact so as to stabilize the robots’ grip on the object. We show that such swift grasping speeds up the pick-and-place process and reduces energy expenditure for tossing. Continuous control of the reach, grab, and toss motion is achieved by combining a sequence of time-invariant dynamical systems (DSs) in a single control framework. We introduce a state-dependent modulation function to control the generated velocity in different directions. The framework is validated in simulation and on a real dual-arm system. We show that we can precisely toss objects within a workspace of [Formula Omitted]. Moreover, we show that the algorithm can adapt on-the-fly to changes in object location.
The development of quantum processors for practical fluid flow problems is a promising yet distant goal. Recent advances in quantum linear solvers have highlighted their potential for classical fluid dynamics. In this...
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The development of quantum processors for practical fluid flow problems is a promising yet distant goal. Recent advances in quantum linear solvers have highlighted their potential for classical fluid dynamics. In this study, we evaluate the Harrow-Hassidim-Lloyd (HHL) quantum linear systems algorithm (QLSA) for solving the idealized Hele-Shaw flow. Our focus is on the accuracy and computational cost of the HHL solver, which we find to be sensitive to the condition number, scaling exponentially with problem size. This emphasizes the need for preconditioning to enhance the practical use of QLSAs in fluid flow applications. Moreover, we perform shots-based simulations on quantum simulators and test the HHL solver on superconducting quantum devices, where noise, large circuit depths, and gate errors limit performance. Error suppression and mitigation techniques improve accuracy, suggesting that such fluid flow problems can benchmark noise mitigation efforts. Our findings provide a foundation for future, more complex application of QLSAs in fluid flow simulations. Published under an exclusive license by AIP Publishing.
In this study, we present a semi-supervised medical image segmentation framework called CycleMatch, which aims to tackle the dependency of fully supervised methods on a large amount of labeled data. By integrating a c...
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In this study, we present a semi-supervised medical image segmentation framework called CycleMatch, which aims to tackle the dependency of fully supervised methods on a large amount of labeled data. By integrating a cyclic pseudo-label distillation mechanism with image-level and feature-level perturbations, CycleMatch effectively leverages unlabeled data to enhance model performance and robustness. Experimental results demonstrate that CycleMatch outperforms existing semi-supervised methods across various data annotation ratios, particularly excelling in scenarios with limited labeled data. Additionally, an in-depth analysis of feature perturbation types and parameter choices further validates CycleMatch's effectiveness and adaptability in handling different medical image datasets. Overall, CycleMatch offers a new solution for medical image segmentation, showcasing the potential for achieving efficient and accurate segmentation even with limited data.
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