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检索条件"主题词=Signal Processing Algorithms"

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Time Based Solar Tracking System Using IOT

Time Based Solar Tracking System Using IOT

引用

International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Mulinaidu Guri Sreenu Thamarana Varalakshmi Sarikonda Mamathadevi Patnala Krishna Vakada Suri Demudu Lagudu Electrical and Electronics Engineering Vignan's Institute of Engineering For Women (A) Visakhapatnam India Electrical and Electronics Engineering Visakha Institute of Engineering and Technology (A) Visakhapatnam India Electrical and Electronics Engineering Aditya University India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The degradation of fossil fuels and the increase in the population creates the plenty of load demand. The balance between electricity generation and load demand is achieved by interconnection of regenerative energy resources. The most plentiful and pure type of renewable energy is photon energy that may gotten from the Sun. This energy is converted by solar panels to produce electric power, which is useful for a variety of electrical uses, especially in rural locations. When employing a standstill solar panel construction, maximum solar power may only be produced for brief periods of time when the Sun is vertical to the panel. Many solar energy tracing systems have been created and suggested throughout the years, and some of them have been examined in the literature. A light dependent resistor (LDR) based solar tracing system fails in tracking solar energy as it have slow response and it may have struggle to track solar energy in cloudy conditions. MPPT based solar tracking system implementation is cost effective and more complexity. In this paper an intelligent process-based design known as time based solar tracing system with IoT is suggested and designed in order to boost the power output regardless of weather and environmental circumstances.

关键词： Costs Surveillance Solar farms signal processing algorithms Solar energy Solar tracking signal processing Solar panels Sun System implementation

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Enhanced Transformer-Based UAV-to-UAV Tracking Using Distractor Elimination and Trajectory Prediction

Enhanced Transformer-Based UAV-to-UAV Tracking Using Distrac...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Wuwei Wang Ruiye Gao Chi Du Yuanqing Li School of Automation Xi’an University of Post and Telecommunications Xi’an China School of Communications and Information Engineering Xi’an University of Post and Telecommunications Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

With the advancement of UAV technology, utilizing UAVs for surveillance, capture, or destruction of other UAVs has become a significant focus in the counter-UAV domain. A core challenge within this domain is the visual target tracking of a UAV by the other UAV. However, due to the highly dynamic relative motion between the source UAV and the target UAV, along with the small scale of the UAV in images and background distractors, existing tracking algorithms, including the most popular Transformer-based trackers, often struggle to continuously and robustly track the target. To address these issues, this paper proposes an enhanced Transformer-based UAV-to-UAV tracking method using distractor elimination and trajectory prediction. Specifically, a target-surrounding matching mechanism is introduced to help the tracker eliminate distractors. Additionally, to tackle the high dynamic tracking problem of the camera and target, a Transformer-based trajectory prediction module is proposed to accurately predict the target’s location even when the tracker loses the target. Experimental results demonstrate that the proposed method achieves superior performance in UAVto-UAV target tracking scenarios compared to state-of-the-art algorithms

关键词： Visualization Target tracking Heuristic algorithms Surveillance signal processing algorithms signal processing Transformers Autonomous aerial vehicles Prediction algorithms Trajectory

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Adversarial Attack Based-on Transferability for Specific Emitter Identification

Adversarial Attack Based-on Transferability for Specific Emi...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Shaolong Liu Tao Zhang Jiang Zhang Xiaoqiang Qiao Hao Wu Yihang Du The Sixty-third Research Institute of National University of Defense Technology Nanjing China Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Deep learning model for specific emitters identification(SEI) is vulnerable to adversarial samples generated by the application of subtle perturbations. However, it is observed that gradient-based adversarial samples generated by white box are poorly effective when they transfer to attack black box. This situation creates the misleading impression that the SEI model possesses excellent robustness. In this study, we propose Virtual Model Gradient Attack (VGA) to improve the attack success rates in black box and enhance the transferability. Firstly, virtual model endows the white box with multiple decision boundaries, aiming to extract multiple gradient information from a single model. Then, VGA achieves fast convergence of the gradients and avoids the occurrence of local optima during the convergence process through the implementation of a gradient update policy. Finally, the simulation results demonstrate that VGA outperforms the existing transferability-based attack algorithms, and the adversarial samples generated by VGA has excellent concealment.

关键词： Wireless communication Transmitters Perturbation methods Simulation signal processing algorithms Receivers signal processing Robustness Convergence Glass box

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High Altitude Platform Station-Greedy Clustering of Wireless Sensor Networks for the Massive IoT

High Altitude Platform Station-Greedy Clustering of Wireless...

引用

International Conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Anastassia Gharib Communications Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Wireless sensor networks (WSNs) play an important role in the Internet of Things (IoT). These are networks of sensor nodes that are clustered to collect and exchange locally sensed data. In each cluster, a cluster head (CH) gathers data from its cluster members, aggregates it, and sends it to the sink node. To serve IoT applications, the sink node then shares this data with the other CHs. Nevertheless, clustering a massive collection of sensor nodes is challenging. This is because these sensor nodes have limited energy resources and are distributed over a vast area. Recently, High Altitude Platform Stations (HAPS) have been shown to improve the connectivity in WSNs by serving as non-terrestrial sink nodes. The quasi-stationary nature of these non-terrestrial platforms can offer vast geographical coverage, and thus, improve WSNs' transmission reliability. This paper proposes HAPS-greedy clustering (HAPS-GC) of WSNs to support massive IoT applications. In contrast to existing HAPSbased WSN clustering schemes, HAPS-GC considers not only the connectivity between sensor nodes within each cluster but also their connectivity with HAPS. Simulation results show that the proposed HAPS-GC approach can significantly increase the WSN throughput while maintaining WSN energy consumption similar to the existing HAPS-based WSN clustering schemes and a scenario, where a terrestrial sink node is used.

关键词： Wireless sensor networks Energy consumption Energy resources Simulation signal processing algorithms signal processing Throughput Internet of Things Reliability Optimization

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Deep Learning-Based Pulmonary Arterial Segmentation in Computed Tomography Images

Deep Learning-Based Pulmonary Arterial Segmentation in Compu...

引用

Symposium of Image, signal processing, and Artificial Vision (STSIVA)

作者： Mishell Merchan Juan Suarez Said Pertuz Escuela de Ingenierías Eléctrica Electrónica y de Telecomunicaciones Universidad Industrial de Santander Bucaramanga Santander

ISBN: (数字)9798350355215

ISBN: (纸本)9798350355222

Pulmonary Embolism (PE) is a life-threatening condition in which a blood clot blocks an artery in the lungs. It remains one of the most challenging conditions to diagnose and treat in the emergency department. Early detection and timely treatment are critical to improving patient outcomes. In this work, we seek to develop and implement an algorithm for automated pulmonary artery segmentation as a crucial step toward PE identification. We had access to a database of 130 CT volumes with refined labeling of pulmonary arteries. We propose the implementation of an architecture called ResD-Unet, based on the Unet network, complemented with residual blocks and interconnected convolution layers. The segmentation quality was measured using the Dice similarity coefficient (DSC). The proposed architecture yielded a DSC of 0.76. Although the results obtained are modest, they represent a first step towards solving the problem and underline the feasibility and potential for future improvements of the proposed approach.

关键词： Image segmentation Databases Convolution Computed tomography Neural networks Lung signal processing algorithms

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Asynchronous DRL and Trajectory Prediction Based Vehicle Edge Computing Task Offloading

Asynchronous DRL and Trajectory Prediction Based Vehicle Edg...

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Wenlong Liu Haifeng Sun Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

With the rapid expansion of the Internet of Vehicles (IoVs), the complex data generated by in-vehicle devices has surged, challenging vehicle computational resources to meet real-time processing demands. To tackle this challenge, Vehicular Edge Computing (VEC) provides improved computational capabilities via roadside units (RSUs) and mobile vehicles. Nevertheless, the rapid movement of these vehicles restricts the communication with base stations, making task offloading a more complex endeavor. This study introduces a task offloading strategy that utilizes trajectory prediction to boost task completion rates by forecasting the base stations that a vehicle is likely to encounter. Furthermore, we propose an optimization method grounded in deep reinforcement learning, specifically A3C, to streamline the offloading process. Our simulation findings reveal a notable increase in task completion rates, coupled with shorter completion times.

关键词： Base stations signal processing algorithms signal processing Telematics Prediction algorithms Real-time systems Trajectory Delays Long short term memory Edge computing

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A local t-diagnosis algorithm for corruption detection on networks

A local t-diagnosis algorithm for corruption detection on ne...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Jianyu Zhou Qiang Zhu School of Mathematics and Statistics Xidian University Xi’an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In an era where networks are crucial to the functioning of critical infrastructure, the ability to quickly and accurately identify compromised nodes using only local information is essential. This research aims to integrate insights from the SPMC model and corruption detection frameworks, proposing a novel algorithm that allows nodes to autonomously assess their status based on localized interactions. The SPMC model, an extension of the PMC model, is derived by introducing two key assumptions: (1) each adjacent node pair is assumed to be mutually tested; and (2) in one-step diagnosis, only the minimal consistent fault set corresponding to a given syndrome is identified. In this paper, we propose a diagnosis algorithm based on local diagnosis techniques to efficiently evaluate the state of a vertex in the system. The algorithm is proven to be correct and is not reliant on any structural assumptions or conjectures, making it applicable to any network. Furthermore, we demonstrate the application of our results to the hypercube Q n .

关键词： Fault diagnosis signal processing algorithms signal processing Hypercubes Critical infrastructure Intelligent control

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VDL Mode 2 Equalizer

VDL Mode 2 Equalizer

引用

Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Dongsong Zeng Stephen L. Giles Jack McQueston The MITRE Corporation McLean VA

ISBN: (数字)9798350393095

ISBN: (纸本)9798350393101

Very High Frequency Digital Link Mode 2 (VDL Mode 2 or VDL2) is an Air/Ground data link that is widely used in air traffic control and airline operation control. As the air traffic management system is becoming increasingly automated and connected, the performance of the VDL2 network has become more and more important for the safety, security, and efficiency of airspace operations. Recent VDL2 performance monitoring has revealed the issue of channel fading - a major problem that causes missed message receptions and reduces the VDL2 network performance. When the signal propagation channel between a transmitter and a receiver affects the signal being transmitted, channel fading occurs. This can be due to reflections off terrain, shadowing, the Doppler effects, multipath effects, or atmospheric conditions, which can all contribute to increased inter-symbol interference at the receiver. This inter-symbol interference distorts the received signal, making it difficult for the receiving device to detect and decode the message, resulting in increased missed message reception *** this paper, we present an innovative VDL2 equalizer technique which has the potential to reduce the missed reception rate of VDL2 signals that are affected by channel fading. Equalizer is a digital signal processing technique which is widely used to compensate the signal distortion caused by the propagation channel. This paper contains a comprehensive description of the proposed VDL2 equalizer. To ascertain the effectiveness of the proposed equalizer, numerical models for both the Least Mean Square (LMS) and Recursive Least Square (RLS) equalization algorithms have been developed. Our simulation results demonstrate that the VDL2 equalizer is capable of counteracting the effects of channel fading and improving the performance of the VDL2 network.

关键词： Fading channels Equalizers Transmitters Surveillance Simulation signal processing algorithms Receivers

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ABConv: Attention Based Convolution for Automatic Modulation Recognition

ABConv: Attention Based Convolution for Automatic Modulation...

引用

IEEE International Conference on Communications Workshops, ICC

作者： Chengyu Guo Shuai Han Weixiao Meng Cheng Li School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering Memorial University Newfoundland Canada

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Automatic modulation recognition (AMR), an intermediate process of signal detection and signal demodulation, has been widely used in various fields. We introduce ABConv, a novel machine learning (ML) filter designed for AMR tasks, leveraging Attention-Based Convolution. Unlike the feature extraction approach using deep learning in AMR tasks, we focus on signal processing methods. Drawing parallels between attention and autocorrelation functions, our model dynamically generates convolution kernels based on attention scores to perform channel blind equalization. ABConv demonstrates state-of-the-art (SOTA) performance with in AMR tasks, surpassing multiple existing advanced algorithms. Additionally, we provide a preliminary visual analysis.

关键词： Training Visualization Convolution Conferences Heuristic algorithms signal processing algorithms Feature extraction

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Layer-Wise Personalized Federated Learning for DAG Blockchain-Enabled Internet of Vehicles

Layer-Wise Personalized Federated Learning for DAG Blockchai...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Xiaoge Huang Wenjing Li Caiwei Yu Chengchao Liang Qianbin Chen School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In the Internet of Vehicles (IoV), personalized federated learning (PFL) can generate personalized models tailored to different local data distributions, thereby improving driving decisions. Existing approaches often perform weighted aggregation on similar vehicles, where the weights are determined by the entire model parameters or loss values. Such model-wise aggre-gation results in inaccurate combination weights, as it overlooks differences in layer-level functionality and imbalances in inter-user accuracy. Additionally, the openness of IoV poses significant threats to system security. In this paper, we propose a blockchain-enabled layer-wise PFL (BLPFL) algorithm to improve system security, model accuracy, and user fairness. Specifically, to preserve personalized knowledge, each local model is split into a globally shared feature extractor and a locally private decision head. To secure the sharing of extractors, Directed Acyclic Graph (DAG)-based blockchain technology is employed. Furthermore, a fine-grained personalized model aggregation mechanism is designed to enhance the model accuracy and user fairness, which explores vehicle similarity at layer granularity and considers special vehicles with severely skewed label distributions. Finally, simulation results demonstrate the effectiveness of the proposed approach in highly heterogeneous scenarios.

关键词： Wireless communication Accuracy Federated learning Simulation signal processing algorithms signal processing Feature extraction Data models Security Internet of Vehicles

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