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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3487 条记录，以下是41-50 订阅

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Can AI See What We Can’t? Leveraging Deep Learning and Multi-Temporal Satellite Data to Revolutionize Crop Type Mapping and Yield Prediction

Can AI See What We Can’t? Leveraging Deep Learning and Mult...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Gautam Siddharth Kashyap Harsh Joshi Manaswi Kulahara Rajkumar Dhakar Atul Sajjanhar Jiechao Gao Sarthak Jain Shahab Saquib Sohail Jamia Hamdard New Delhi India Bharati Vidyapeeth New Delhi India TERI New Delhi India IARI New Delhi India Deakin University Burwood Australia University of Virginia Virginia USA GGSIPU New Delhi India VIT Bhopal University Sehore India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Precise mapping of crop types and estimating yields are important in gauging agricultural diversity and yield potential, especially in regions dominated by small-scale farming. Nevertheless, these tasks are challenging due to factors such as small field sizes, inter-cropping, and a lack of sufficient ground truth labels for certain regions. In this paper, we propose an approach that combines advanced deep learning algorithms with Sentinel-2 and MODIS satellite data for improving the accuracy of crop type mapping and yield prediction. We used datasets from the main growing season of 2017 in Kenya (Bungoma, Busia and Siaya) coupled with county level yield data from US, Argentina and Brazil spanning from 2005 to 2016. Our models (CNN, SegNet, MaskRCNN, ResNet, UNet) were evaluated on both tasks i.e., classification of crop types and predicting yields.

关键词： Deep learning Satellites Crops signal processing algorithms Soil signal processing Satellite images Speech processing Farming Testing

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FLoRaSat 2: Simulating Cross-Linked Direct-to-Satellite IoT LEO Constellations

FLoRaSat 2: Simulating Cross-Linked Direct-to-Satellite IoT ...

引用

advanced Satellite Mobile Systems (ASMS)

作者： Alexander Y. Choquenaira-Florez Robin Ohs Juan A. Fraire Hervé Rivano Inria INSA Lyon CITI Villeurbanne France Saarland University Saarbrücken Germany CONICET Universidad Nacional de Córdoba Córdoba Argentina

ISBN: (数字)9798331522353

ISBN: (纸本)9798331522384

Direct-to-Satellite IoT (DTS-IOT) represents a promising solution for data transmission in remote regions where terrestrial infrastructure deployment is unfeasible. In DTS-IOT scenarios, Low-Earth Orbit (LEO) satellites function as in-orbit gateways. Addressing the need for practical simulation tools, we present FLORASAT 2, an open-source, event-driven, end-to-end simulation tool leveraging OMNET++. The original FLO-RASAT met many DTS-IOT Medium Access Control (MAC) requirements. Still, this enhanced version introduces advanced Inter-Satellite Link (ISL) communication modules, including a helper for constellation creation, dynamic ISL topology control, routing, and analytics, facilitating the thorough evaluation of constellation-grade DTS-IOT networks. These new features allow detailed simulation scenario configuration, flexible support for developing and including diverse routing algorithms, and the tooling to perform automated data analysis from parametric simulations. Overall, the simulator enables the analysis of complex behaviors in DTS-IOT environments, optimizing performance and enhancing connectivity and efficiency in large-scale satellite IoT constellation networks.

关键词： Satellites Network topology Space communications Low earth orbit satellites signal processing algorithms signal processing Routing Orbits Topology Satellite communications

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Data Glove-based Personalized Continuous Gesture Segmentation

Data Glove-based Personalized Continuous Gesture Segmentatio...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liufeng Fan Zhan Zhang Yiwei Wang Decheng Zuo Yinran Wang Zhongyuan Chen Facculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, gesture recognition based on data gloves has attracted increasing attention as a human-computer interaction (HCI) method that is natural, convenient, stable, robust, easy to recognize, and applicable to various usage environments. This research first proposes an advanced smart data glove that integrates cutting-edge flexible capacitive sensors on the fingertips and a 6-axis IMU on the back of the hand to recognize gestures. Secondly, this study proposes a personalized continuous gesture segmentation (PCGS) model that can adaptively calculate the most appropriate gesture segmenting threshold based on the current user and introduces the multi-sliding window theory and kinematic knowledge to perform personalized gesture segmentation. The accuracy of gesture segmentation can reach 94.3%. The result shows that our PCGS model achieves an average segmentation accuracy of 94.3% and outperforms the state-of-the-art pproaches by 11.2% to 18.5%.

关键词： Adaptation models Accuracy signal processing algorithms Speech recognition Data gloves signal processing Stability analysis Robustness Speech processing Intelligent sensors

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Millimeter-Wave SDR Testbed Enabling Deep Q-Learning for Angle-of-Arrival Detection

Millimeter-Wave SDR Testbed Enabling Deep Q-Learning for Ang...

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IEEE conference on Wireless Communications and Networking

作者： Marc H. Jean Murat Yuksel University of Central Florida Orlando FL USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Millimeter-wave (mmWave) signals experience severe environmental path loss. To address this issue highly directional antennas with beamforming capabilities are used to align the transmit and receive antenna beams, allowing the signal to travel longer distances. However, advanced algorithms are needed to detect the angle-of-arrival (AoA) for better alignment of the antenna beams. Combining software-defined radios (SDR) with mmWave radio frequency (RF) antenna systems can enable researchers to develop advanced algorithms for real-world scenarios. Using low-cost mmWave RF front-end components and an SDR, we develop a testbed that uses open-source tools and high-level programming languages for beamforming algorithm development. In the testbed, we prototype a deep Q-network (DQN) algorithm for mmWave AoA detection. We evaluate the performance of the algorithm by fine tuning the reinforcement learning (RL) hyperparameters.

关键词： Radio frequency Computer languages Q-learning Array signal processing Receiving antennas Prototypes Directional antennas Millimeter wave communication Tuning Software radio

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AI-Powered Warehouse Management: Autonomous Mobile Robots and advanced Optimization with NFC, MILP, and Swarm algorithms

AI-Powered Warehouse Management: Autonomous Mobile Robots an...

引用

International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Jyothi Bobba Ramya Lakshmi Bolla Rajeswaran Ayyadurai Karthikeyan Parthasarathy Naresh Kumar Reddy Panga Sandhya Rani Lead IT Corporation Illinois USA ERP Analysts Ohio USA IL Health & Beauty Natural Oils Co Inc California USA LTIMindtree Tampa USA Virtusa Corporation New York USA School of Computing and Information Technology REVA University India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

Warehouse automation, coupled with slightly increased outlay executions and a task distribution process, has become an exceedingly fundamental supply chain operation in modern-day reality. In this article, an AI-Refined framework is proposed that combines Automated Mobile Robots, Near-field Communication, Mixed Integer Linear Programming, and swarm algorithms that would result in maximal efficiency within the warehouse. The proposed design examines swarm techniques for task distribution, and NFC for seamless data sharing, MILP for tactical optimization, combined with AMRs for automation; all directed toward reducing inefficiencies and enabling real-time decision-making. The main objectives of this framework include maximization of resource utilization, enhancement of efficiency, scalability, adaptability, and support for dynamic supply chain management. Experimental results showed that the proposed framework performed better than conventional techniques, with an accuracy of 94%, an F1 score of 95%, and a scalability score of 92%. This gives an insight that integrated methods can really help ameliorate the operations in the warehouse. Finally, this research has shown the enormous capability of AI automation with regards to the resource management, process enhancement, and solving critical issues set in warehousing that will start offering a more intelligent and flexible logistics solution.

关键词： Automation Scalability Heuristic algorithms Supply chains Warehousing signal processing algorithms Real-time systems Resource management Mobile robots Optimization

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AI in Telecommunication: Revolutionizing signal processing and Communication Networks in Smart Grid

AI in Telecommunication: Revolutionizing Signal Processing a...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Shweta Dewangan Geetha Manoharan Srishti Shaifali Minz Sanjeev Kumar Mohammad Badruddoza Talukder Krishan Arora Department of Commerce ICFAI University Raipur India School of Business SR University Warangal Telangana India Department of Commerce Govt. Rajmohini Devi Girls P.G college Ambikapur School of Hotel Management and Tourism Lovely Professional University Phagwara India College of Tourism and Hospitality Management International University of Business Agriculture and Technology Dhaka Bangladesh School of Electronics and Electrical Engineering Lovely Professional University Punjab

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Artificial Intelligence (AI) is revolutionising the telecommunications industry by enhancing signal processing, network management, and overall system performance. With the rise of 5G and IoT, AI has become a critical technology driving innovation and efficiency. AI algorithms have improved error correction, data compression techniques, and bandwidth allocation, ensuring networks can handle growing data traffic volumes. AI's ability to predict, analyse, and adapt to real-time data has improved network management capabilities, allowing more intelligent resource allocation, reduced latency, and increased network reliability. AI-driven network automation has enabled providers to shift from reactive to proactive management, minimising downtime and enhancing user experience. This shift boosts operational efficiency and reduces costs by enabling predictive maintenance and optimising energy consumption in data centres and network infrastructure. The future of telecommunications will be shaped by AI technologies like machine learning, deep learning, and natural language processing, leading to advanced services like self-healing networks, real-time traffic management, and enhanced cybersecurity measures. As the telecommunication industry embraces AI-driven innovations, it will unlock new opportunities for growth and transform the everyday communication experience for billions of people worldwide.

关键词： Technological innovation signal processing algorithms signal processing Prediction algorithms Real-time systems Telecommunications Smart grids Artificial intelligence Monitoring Predictive maintenance

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Traffic Sign and Obstacle Detection for Autonomous Vehicle Navigation using Edge Computing

Traffic Sign and Obstacle Detection for Autonomous Vehicle N...

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International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： M. Vijayalakshmi M. Bharathwaj D. Anjelin Deva Dharshini Department of IST CEG Anna University Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehicle (AV) simulation framework utilizing the Carla simulator, advanced image processing, and state-of-the-art machine learning models to enhance autonomous navigation capabilities. The system initiates by loading a specified virtual map within the Carla simulator and deploying an ego vehicle equipped with a camera sensor to capture environmental images. These images undergo preprocessing techniques, to ensure suitability for analysis. Traffic sign recognition system uses a pre-trained YOLOv7 model, enabling the AV to identify and respond to various traffic signals amidst occlusion, adverse weather, and variations in placement and size. Concurrently, a pre-trained Faster R-CNN model detects obstacles such as vehicles and pedestrians along the vehicle’s path. A dedicated distance estimation module further analyzes these preprocessed images to calculate the relative distances of detected obstacles using depth estimation techniques, providing crucial spatial information. The decision-making algorithm resolves conflicts between recommended actions from traffic sign recognition and obstacle detection modules, prioritizing safety-critical commands to ensure operational safety and efficiency. The models are stored on the network edge, facilitating efficient deployment and real-time processing. This framework demonstrates an effective integration of simulation, image analysis, and decision-making, advancing the reliability and performance of autonomous navigation systems.

关键词： Navigation Heuristic algorithms Decision making signal processing algorithms Cameras Real-time systems Road safety Classification algorithms Autonomous vehicles Edge computing

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A High-Density Electromyography Sensor Based on MEMS E-Skin for Detecting Activity of Motor Units

A High-Density Electromyography Sensor Based on MEMS E-Skin ...

引用

IEEE International conference on Micro Electro Mechanical Systems

作者： Yirong Wang Shuihan Shao Chunpeng Jiang Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper introduces a compact, ultrathin, and stretchable high-density surface electromyography (sEMG) electrode designed for targeted signal acquisition from 16 or more channels in localized muscle regions around the mouth and neck during swallowing. These signals are processed into motor unit potentials through advanced decomposition algorithms. The electrode features a serpentine structure approximately 6 micrometers thick, enabling conformal contact with the skin and accommodating cyclic stretching up to 160%, thereby minimizing motion artifacts and ensuring high-quality signal capture. Surface modifications with iridium oxide and platinum black are applied to reduce contact impedance. The high channel density and small form factor facilitate detailed analysis of deep motor unit activity in targeted muscles using high-density sEMG decomposition techniques.

关键词： Electrodes Micromechanical devices Surface impedance Electronic skin signal processing algorithms Muscles Motors Electromyography Impedance Surface treatment

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Multiple Sclerosis Detection with Reinforcement Learning and Differential Evolution

Multiple Sclerosis Detection with Reinforcement Learning and...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jing Yang Jin Yang Chenwei Wu Gaozhe Jiang Yaning Lv Bingyan Liu Feng Xu Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis USA Evident Battery Inc. Massachusetts USA Institute of Operations Research and Analytics National University of Singapore Singapore School of Mathematics and Statistics Xi'an University of Electronic Science and Technology Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multiple Sclerosis (MS) disrupts nerve communication, potentially leading to permanent damage. Convolutional Neural Networks (CNNs) are commonly recommended to accelerate magnetic resonance imaging (MRI) analysis for MS. Traditional CNN-based methods often face challenges with feature selection, imbalanced classification, and hyperparameter optimization, which can reduce their effectiveness. To address these challenges, this paper introduces a reinforcement learning (RL) strategy for tackling feature selection and class imbalance, complemented by an advanced Differential Evolution (DE) algorithm for hyperparameter tuning. The RL algorithm dynamically adapts, enhancing feature selection and class balance, and consistently outperforms traditional static methods by continually optimizing its response to the complex patterns in MS detection. Furthermore, the DE is enhanced with a novel mutation mechanism that utilizes k-means clustering to identify significant clusters. Tested on the SNAC-MS and ISBI datasets, our model demonstrates robust accuracy with average F-measures of 92.873% and 93.783%, respectively, highlighting its effectiveness and adaptability in diagnosing MS.

关键词： Adaptation models Multiple sclerosis Heuristic algorithms Magnetic resonance imaging signal processing algorithms Clustering algorithms Reinforcement learning Feature extraction Hyperparameter optimization Classification algorithms

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Robust Calibration for Enhanced Imaging in Near-Field D-Band MIMO Radar Systems

Robust Calibration for Enhanced Imaging in Near-Field D-Band...

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European conference on Antennas and Propagation, EuCAP

作者： Lorena Perez-Eijo Marcos Arias Borja Gonzalez-Valdes José Vázquez-Cabo Ignacio Sardinero–Meirás Jesus Grajal AtlanTTic Research Center Universidade de Vigo Vigo Spain Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This work introduces a calibration algorithm designed to improve high-precision imaging in real-time MIMO radar systems. The algorithm applies the Inverse Fourier Transform (IFFT) and advanced signal processing techniques to efficiently extract and process key data from raw measurements, focusing on critical Regions Of Interest (ROI) while filtering out unnecessary information. A robust phase correction mechanism ensures precise calibration and alignment, preventing phase errors that could otherwise lower image quality. The algorithm provides an effective balance between computational efficiency and the high accuracy required by modern MIMO radars, paving the way for further improvements in real-time imaging.

关键词： MIMO radar signal processing algorithms Coherence Radar imaging signal processing Real-time systems Hardware Calibration Spatial resolution Image reconstruction

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