Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling *** work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction...
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Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling *** work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction.A system architecture fusing visible light positioning,multi-agent path finding via reinforcement learning,and 360°camera techniques for 3D reconstruction is *** visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional ***,a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem,with communications among agents *** 3D reconstruction pipeline utilizes equirectangular projection from 360°cameras to facilitate depth-independent reconstruction from posed monocular images using neural *** validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our *** challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding *** summary,this research advances fundamental techniques for multi-robot indoor 3D modeling,contributing to automated,data-driven applications through coordinated robot navigation,perception,and modeling.
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tum...
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Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement...
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Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement in realistic multipath environments. Array decorrelation techniques have been proposed, achieving correlation reductions by either tilting the antenna beams or shifting the phase centers away from each other. Hence, these methods are mainly limited to MIMO terminals with small arrays. To avoid such problems, this work proposes a decorrelation optimization technique based on phase correcting surface(PCS)that can be applied to large MIMO arrays, enhancing their MIMO performances in a realistic(non-isotropic)multipath environment. First, by using a near-field channel model and an optimization algorithm, a near-field phase distribution improving the MIMO capacity is obtained. Then the PCS(consisting of square elements)is used to cover the array's aperture, achieving the desired near-field phase *** examples demonstrate the effectiveness of this PCS-based near-field optimization technique. One is a1 × 4 dual-polarized patch array(working at 2.4 GHz)covered by a 2 × 4 PCS with 0.6λ center-to-center distance. The other is a 2 × 8 dual-polarized dipole array, for which a 4 × 8 PCS with 0.4λ center-to-center distance is designed. Their MIMO capacities can be effectively enhanced by 8% and 10% in single-cell and multi-cell scenarios, respectively. The PCS has insignificant effects on mutual coupling, matching, and the average radiation efficiency of the patch array, and increases the antenna gain by about 2.5 dB while keeping broadside radiations to ensure good cellular coverage, which benefits the MIMO performance of the *** proposed technique offers a new perspective for improving large MIMO arrays in realistic multipath in a statistical sense.
This paper introduces Deep Convolutional Generative Adversarial Networks (DCGAN) as a potential solution for wireless systems aiming to enhance the Block Error Rate (BLER). The DCGAN under consideration consists of a ...
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Sparse Matrix-Vector/Matrix Multiplication, namely SpMMul, has become a fundamental operation during model inference in various domains. Previous studies have explored numerous optimizations to accelerate it. However,...
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Sparse Matrix-Vector/Matrix Multiplication, namely SpMMul, has become a fundamental operation during model inference in various domains. Previous studies have explored numerous optimizations to accelerate it. However, to enable efficient end-to-end inference, the following challenges remain unsolved: (1) Incomplete design space and time-consuming preprocessing. Previous methods optimize SpMMul in limited loops and neglect the potential space exploration for further optimization, resulting in >30% waste of computing power. Additionally, the preprocessing overhead in SparseTIR and DTC-SpMM is 1000× larger than sparse computing. (2) Incompatibility between static dataflow and dynamic input. A static dataflow can not always be efficient to all input, leading to >80% performance loss. (3) Simplistic algorithm performance analysis. Previous studies primarily analyze performance from algorithmic advantages, without considering other aspects like hardware and data features. To tackle the above challenges, we present DA-SpMMul, a Data-Aware heuristic GPU implementation for SpMMul in multi-platforms. DA-SpMMul creatively proposes: (1) Complete design space based on theoretical computations and nontrivial implementations without preprocessing. We propose three orthogonal design principles based on theoretical computations and provide nontrivial implementations on standard formats, eliminating the complex preprocessing. (2) Feature-enabled adaptive algorithm selection mechanism. We design a heuristic model to enable algorithm selection considering various features. (3) Comprehensive algorithm performance analysis. We extract the features from multiple perspectives and present a comprehensive performance analysis of all algorithms. DA-SpMMul supports PyTorch on both NVIDIA and AMD and achieves an average speedup of 3.33× and 3.02× over NVIDIA cuSPARSE, and 12.05× and 8.32× over AMD rocSPARSE for SpMV and SpMM, and up to 1.48× speedup against the state-of-the-art open-source algo
The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient *** kickboards are gradually growing in popularity in tourist and education...
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The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient *** kickboards are gradually growing in popularity in tourist and education-centric *** the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is *** to its freefloating nature,the shared electric kickboard is a common and practical means of *** plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is *** demand accurately with small data is *** data is necessary for training machine learning algorithms for effective *** generation is a method for expanding the amount of data that will be further accessible for *** this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original *** proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction *** modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster ***,we applied a regression-based blending ensemble technique that can help us to improve performance of demand *** used various evaluation criteria and visual representations to compare our proposed model’s *** data generated by our suggested GAN model is also *** TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.
Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interf...
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A novel synthesis method for wideband bandpass filter (BPF) with two in-band conjugate complex transmission zeros is proposed for realizing frequency- and attenuation-reconfigurable in-band notch. A new characteristic...
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We present a novel method to generate affective phrase sets using Large Language Models (LLMs), for use in text entry study transcription tasks. These phrases could be used to adjust participant emotions, and enable p...
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G cellular networks have achieved significant improvements in capacity and performance, while they have incurred a substantial increase in energy consumption at the base station (BS) level. To address this escalating ...
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