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Height Change Feature Based Free space Detection 11

Height Change Feature Based Free Space Detection

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11th International conference on Control, Mechatronics and Automation (ICCMA)

作者： Schreck, Steven Reichert, Hannes Hetzel, Manuel Doll, Konrad Sick, Bernhard Univ Appl Sci Aschaffenburg Aschaffenburg Germany Univ Kassel Kassel Germany

ISBN: (纸本)9798350315684

In the context of autonomous forklifts, ensuring non-collision during travel, pick, and place operations is crucial. To accomplish this, the forklift must be able to detect and locate areas of free space and potential obstacles in its environment. However, this is particularly challenging in highly dynamic environments, such as factory sites and production halls, due to numerous industrial trucks and workers moving throughout the area. In this paper, we present a novel method for free space detection, which consists of the following steps. We introduce a novel technique for surface normal estimation relying on spherical projected LiDAR data. Subsequently, we employ the estimated surface normals to detect free space. The presented method is a heuristic approach that does not require labeling and can ensure real-time application due to high processing speed. The effectiveness of the proposed method is demonstrated through its application to a real-world dataset obtained on a factory site both indoors and outdoors, and its evaluation on the Semantic KITTI dataset [2]. We achieved a mean Intersection over Union (mIoU) score of 50.90% on the benchmark dataset, with a processing speed of 105 Hz. In addition, we evaluated our approach on our factory site dataset. Our method achieved a mIoU score of 63.30% at 54 Hz.

关键词： Free space Detection Surface Normal Estimation LiDAR Perception KITTI dataset AGV

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handling MEG data Corrupted with Deep Brain Stimulation Artifacts - Some Pointers 1

Handling MEG Data Corrupted with Deep Brain Stimulation Arti...

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1st International conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Ganguly, Suranjita Jain, Ankita Koppula, Aditya Sasidharan, Sreedev Dattada, Vamsi Vijay Mohan Hojlund, Andreas Sridharan, Kousik Sarathy Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Aarhus University Cognitive Science and Semiotics Department of Linguistics Aarhus Denmark Indian Institute of Technology Hyderabad Dept. of Heritage Science and Technology Hyderabad India

ISBN: (纸本)9798350342796

Deep brain stimulation (DBS) is a therapeutic treatment used in pathological conditions, which is known to improve clinical state, but the effect-mechanisms are unclear. The electrophysiological study of effect-mechanisms is complicated by strong artifactual components blurring the boundary between brain activity and DBS-induced noise. To study the artifactual signatures and the effect of pre-processing steps such as spatiotemporal signal space separation (tSSS) and normalization on the DBS-corrupted MEG data, we deployed a watermelon-based phantom 'implanted' with DBS leads and stimulated the phantom with various DBS stimulation settings. We observe that tSSS can lead to a 'spillover' of artifacts between sensor types and bipolar stimulation leads to lower sub-harmonic (20 Hz) artifacts compared to monopolar stimulation. Normalized spectra, we show here, can be biased if artifactual spectral bins are chosen as the normalizing factor. Alternatively, artifact-free bins can be visually chosen or an unbiased approach such as the FOOOF method can be used. © 2023 IEEE.

关键词： artifcact handling Deep Brain Stimulation MEG Parkinson's disease Phantom

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data-Driven Human Factors Enabled Digital Twin 49

Data-Driven Human Factors Enabled Digital Twin

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49th Annual conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Kolesnikov, Mikhail V. Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350331820

This paper presents a methodology for increasing human-centric production systems flexibility using human factors-enabled digital twins. The paper includes an analysis of the relevant projects that incorporate human-related data collection and processing. The proposed system is capable of collecting human factors-related data from various sources and then use a decision-making algorithm to schedule the tasks according to assessed human operator conditions in real-time. The formed Digital Twin is able to depict the condition of the labourer and production system status in real-time using Visual Components simulation environment. Shown results prove that existing production systems are capable of adapting to the changing condition of the worker flexibly, optimising workflow, distributing tasks with AGVs and cobots, and applying changes in workplace ergonomics to achieve better safety and performance of the worker. © 2023 IEEE.

关键词： Decision making

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E-graze data Cube for regular monitoring of grasslands, a case study in Greece 9

E-graze Data Cube for regular monitoring of grasslands, a ca...

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9th International conference on Remote Sensing and Geoinformation of the Environment, RSCy 2023

作者： Kalopesa, Eleni Tsakiridis, Nikolaos Samarinas, Nikiforos Zalidis, George Tziolas, Nikolaos School of Agriculture Faculty of Agriculture Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki54123 Greece Southwest Florida Research and Education Center Department of Soil and Water Sciences Institute of Food and Agricultural Sciences University of Florida 2685 State Rd 29N ImmokaleeFL34142 United States AGROKAT CO & G.P. Elassona40200 Greece

ISBN: (纸本)9781510668225

Regular monitoring of grassland productivity is important for a sustainable use of their resources and understanding how ongoing and predicted extreme events impact their productivity and the adaptations needed in their management. Recognizing these issues, we propose the development of a powerful and modular digital infrastructure able to provide geospatial products in support of grassland monitoring in Serres regional unit, Greece. The current work firstly aims at providing a tool enabling rapid Earth Observation data access, handling, and pre-processing by significantly facilitating the generation of analysis-ready data using Sentinel-2 imagery data allowing to build the first version of a grasslandoriented data Cube. The overarching objective is to develop biomass and environmental related proxies tailored to grassland ecosystems, using state-of-the-art artificial intelligence and data mining techniques to make the most out of optical high spatial resolution data for the derivation of grassland biophysical parameters and degradation indicators. The final products are summarized in a land use map area of the area derive from a convolution neural network classification algorithm, as well as a set of vegetation indices and bare soil products from the period from 2020-2022. The output will be used to provide technical recommendations to farmers to make better management decisions and will inform indexbased environmental assessment, letting farmers avoiding land abandonment or overgrazing and the related consequences on landscape conservation, soil quality, and biodiversity. © 2023 SPIE.

关键词： Ecosystems

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A Novel Multi-User space-Time Block Coding based Superposition Transmission for Future Generation Wireless Networks 97

A Novel Multi-User Space-Time Block Coding based Superpositi...

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97th IEEE Vehicular Technology conference (VTC-Spring)

作者： Khan, Muhammad Farhan Pesch, Dirk Iqbal, Adeel Iqbal, Sadiq Hamamreh, Jehad M. Univ Coll Cork Confirm Ctr Smart Mfg Cork Ireland COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad Pakistan Antalya Bilim Univ Dept EE Engn Wislabi Com Antalya Turkiye

ISBN: (纸本)9798350311143

The present study proposes a novel multi-user transmission scheme, which employs the utilization of space-time block coding (STBC) in conjunction with orthogonal frequency division multiplexing (OFDM) as the transmission framework. The scheme, referred to as multi-user STBC super-positioned transmission (MU-STBC-ST), utilizes the superposition of user data to serve multiple users with spectral, temporal, and frequency resources similar to non-orthogonal multiplexing. Furthermore, the proposed scheme is deployed in conjunction with multipleinput multiple-output (MIMO) transmission to serve two users, resulting in an improved version of STBC-MIMO. To achieve this, we employ the utilization of uniquely designed auxiliary signals that are superimposed on top of already super-positioned user data during the two transmission slots, with the objective of intelligently canceling the inter-user interference and channel effects at the receiver, while simultaneously making the reception process much simpler, energy-efficient and less power-draining. Additionally, we also propose a simple equalization policy to recover the signals at the receiver while reducing complexity significantly, resulting in low latency and less processing at the receiver. The proposed scheme's performance is evaluated through computer simulation utilizing performance metrics such as bit error rate (BER), throughput error rate (TER), and peak-to-average power ratio (PAPR) and compared with that of conventional STBC.

关键词： Alamouti STBC multi-user MIMO OFDM 6G IoT diversity Super Positioning

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Experimental Evaluation of Moving Target Compensation in High Time-Bandwidth Noise Radar 20

Experimental Evaluation of Moving Target Compensation in Hig...

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20th european Radar conference, EuRAD 2023

作者： Ankel, Martin Jonsson, Robert S. Tholen, Mats Bryllert, Tomas Ulander, Lars M.H. Delsing, Per Chalmers University of Technology Department of Microtechnology and Nanoscience Sweden Surveillance Saab Research and Concepts Sweden KTH Royal Institute of Technology Nanostructure Physics Sweden Intermodulation Products AB Sweden Chalmers University of Technology Earth and Environment Geoscience and Remote Sensing Department of Space Sweden

ISBN: (纸本)9782874870743

In this article, the effect a moving target has on the signal-to-interference-plus-noise-ratio (SINR) for high time-bandwidth noise radars is investigated. To compensate for cell migration we apply a computationally efficient stretch processing algorithm that is tailored for batched processing and suitable for implementation onto a real-time radar processor. The performance of the algorithm is studied using experimental data. In the experiment, pseudorandom noise, with a bandwidth of 100 MHz, is generated and transmitted in real-time. An unmanned aerial vehicle (UAV), flown at a speed of 11.5 m/s, is acting as a target. For an integration time of 1 s, the algorithm is shown to yield an increase in SINR of roughly 13 dB, compared to no compensation. It is also shown that coherent integration times of 2.5 s can be achieved. © 2023 european Microwave Association (EuMA).

关键词： Bandwidth

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Diffusion Hyperfeatures: Searching Through Time and space for Semantic Correspondence 37

Diffusion Hyperfeatures: Searching Through Time and Space fo...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Luo, Grace Dunlap, Lisa Park, Dong Huk Holynski, Aleksander Darrell, Trevor Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781713899921

Diffusion models have been shown to be capable of generating high-quality images, suggesting that they could contain meaningful internal representations. Unfortunately, the feature maps that encode a diffusion model's internal information are spread not only over layers of the network, but also over diffusion timesteps, making it challenging to extract useful descriptors. We propose Diffusion Hyperfeatures, a framework for consolidating multi-scale and multi-timestep feature maps into per-pixel feature descriptors that can be used for downstream tasks. These descriptors can be extracted for both synthetic and real images using the generation and inversion processes. We evaluate the utility of our Diffusion Hyperfeatures on the task of semantic keypoint correspondence: our method achieves superior performance on the SPair-71k real image benchmark. We also demonstrate that our method is flexible and transferable: our feature aggregation network trained on the inversion features of real image pairs can be used on the generation features of synthetic image pairs with unseen objects and compositions. Our code is available at https://***.

关键词： data handling

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Employing Tensor Decomposition and Contextual Inference for Reducing the Semantic Discrepancy in Image processing 2

Employing Tensor Decomposition and Contextual Inference for ...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Suneetha, K. Gupta, Namit University Karnataka Bangalore India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350369960

Despite the growing need for effective image tagging in both commercial and research domains, the inconsistency between image content and text annotations remains a significant issue. We propose an innovative approach that leverages deep learning and tensor decomposition to enhance the quality of image labelling and eliminate the reliance on labor-intensive manual tagging. Our methodology transforms images into tensors, facilitating the formation of a standardized feature space. We employ a sophisticated five-layer neural network architecture that integrates three-level tensor decomposition to match images with their context groups accurately. This structure is adept at capturing the nuanced patterns in data, which is crucial for reducing the semantic discrepancy between visual and textual information. We have rigorously evaluated our algorithm using the Corel-10K dataset, focusing on the specific case of penguin images, to demonstrate the efficacy of our model. The results showcase a marked improvement in the relevance of generated tags, thereby enhancing the retrieval accuracy and mitigating the issue of inappropriate tag assignment. This work not only contributes to the fields of Machine Learning and data Mining by refining feature extraction and context estimation techniques but also has significant implications for the development of NLP algorithms that can better interpret and generate descriptive tags for visual content. Moreover, our approach sets the stage for future explorations into the integration of AI-driven image tagging systems with robotic systems for autonomous data handling and organization. © 2023 IEEE.

关键词： Contextual Estimation Image Understanding Techniques Semantic Gap Minimization Tensor-Based Image Analysis Visual data Interpretation

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Free-space Gigabit data Transmission with a Directly Modulated Interband Cascade Laser Epitaxially Grown on Silicon

Free-Space Gigabit Data Transmission with a Directly Modulat...

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Annual conference of the IEEE-Photonics-Society (IPC)

作者： Zaminga, S. Didier, P. Kim, H. Diaz-Thomas, D. A. Baranov, A. N. Rodriguez, J. B. Tournie, E. Knoetig, H. Spitz, O. Schwarz, B. Cerutti, L. Grillot, F. Inst Polytech Paris LTCI Telecom Paris Palaiseau France mirSense Campus EiffelBatiment E RDC1 Rue Jean Rostand F-91400 Orsay France Univ Cent Florida CREOL Orlando FL 32816 USA Univ Montpellier CNRS UMR 5214 IES F-34000 Montpellier France TU Wien Inst Solid State Elect Gusshausstr 25-25a A-1040 Vienna Austria Univ New Mexico Ctr High Technol Mat Albuquerque NM 87106 USA

This work demonstrates mid-infrared data transmission using an epitaxial interband cascade laser on silicon emitting at 3.5 mu m. Direct modulation at 5 Gbit/s with on-off keying is achieved at room temperature while ... 详细信息

ISBN: (纸本)9798350347227

关键词： Interband cascade laser free-space optics optical communications silicon integration mid-infrared photonics

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ENHANCING RISK ASSESSMENT AND MONITORING FOR CULTURAL HERITAGE SITES THROUGH data CUBES: A MULTIDIMENSIONAL APPROACH 9

ENHANCING RISK ASSESSMENT AND MONITORING FOR CULTURAL HERITA...

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9th International conference on Remote Sensing and Geoinformation of the Environment, RSCy 2023

作者： Leventis, Georgios Argyriou, Athanasios V. Cerra, Daniele Hadjimitsis, Diofantos ERATOSTHENES Centre of Excellence 82 Franklin Roosevelt Limassol3012 Cyprus German Aerospace Center Remote Sensing Technology Institute Oberpfaffenhofen Weßling Germany

ISBN: (纸本)9781510668225

The Eastern Mediterranean, Middle East, and North Africa (EMMENA) regions are rich in Cultural Heritage (CH) sites that have been subject to various threats, including conflicts, natural disasters, and urban development. Effective risk assessment and monitoring are essential to preserve and protect these assets. Towards that direction novel technologies and their integration can be valuable for a holistic framework of managing diverse datasets and providing a robust safeguarding of CH assets. A data cube is a multidimensional representation of data that allows for efficient and flexible analysis, designed to support online analytical processing (OLAP) and data mining. data cubes can be regarded as a threedimensional structure, with each cell representing a unique combination of values from the different dimensions. By creating a data cube that includes several satellite and geospatial data sources, organizations can gain a more holistic understanding of the risks and opportunities associated with CH sites as well as to identify patterns and trends that might not be apparent in individual data sets. Within this context, it becomes apparent that data cubes allow for a multidimensional view of the risk landscape and can be used to create data-driven predictive models forecasting risks and opportunities for CH assets, - in order for them to be preserved and protected for future generations. The risk assessment and monitoring framework used in this study can be easily transferred, in order to monitor CH sites in any sensitive region and can be adapted to include data from other sources and monitor different types of threats, including climate change related, environmental, and social risks. © 2023 SPIE.

关键词： Risk assessment

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