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检索条件"主题词=Signal processing algorithms"

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A Packet-Based Analog m-CAP Visible Light Communication System for Internet of Things

A Packet-Based Analog m-CAP Visible Light Communication Syst...

引用

International Symposium on Communication Systems, Networks and Digital signal processing, CNSDSP

作者： Luís Rodrigues Mónica Figueiredo Luís Nero Alves Zabih Ghassemlooy Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal School of Technology and Management Polytechnic of Leiria Leiria Portugal Faculty of Engineering and Environment Optical Communications Research Group Northumbria University Newcastle upon Tyne U.K.

ISBN: (数字)9798350348743

ISBN: (纸本)9798350348750

The Internet connectivity in electronic devices has been progressively increasing over the years. While radio frequency (RF)-based communications dominate Internet of Things (IoT) applications, they face some issues such as interference and spectrum limitations. Visible light communications (VLC) uses light emitting diodes (LEDs) lights with intensity modulation to convey the signal and it has been demonstrated as a viable alter-native for indoor IoT scenarios. On the other hand, data clock synchronization between the transmitter (Tx) and the receiver (Rx) is crucial for reliable data transmission, mitigating timing errors and packet losses. Typical implementations are complex and require high-speed processing. This paper introduces an algorithm for data clock synchronization in a hybrid multiband carrierless amplitude and phase (m-CAP)/quadrature amplitude modulation(QAM) VLC system for IoT devices, ensuring accurate clock recovery that can be implemented in a low-cost microcontroller. The system achieves synchronization with a clock mismatch tolerance of up to 4200 ppm, supporting frames with a payload length up to 624 octets. By using averaging window sampling instead of a single sample acquisition, it is possible an extension of the payload length by approximately 208 octets.

关键词： signal processing algorithms Symbols Jitter Light emitting diodes Synchronization Internet of Things Reliability

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Efficient Wake-Up Strategy: UAV-Enabled Opportunistic Sensing in IoT Networks

Efficient Wake-Up Strategy: UAV-Enabled Opportunistic Sensin...

引用

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

作者： Krishnendu S. Tharakan Omar Khalifa Hayssam Dahrouj Nour Kouzayha Hesham ElSawy Noha Al-Harthi Zekeriya Aksoy Jaafar Elmirghani Tareq Y. Al-Naffouri School of Computing Queen's University Kingston Canada CEMSE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Electrical Engineering University of Sharjah Sharjah United Arab Technology Research and Innovation Department in Public Safety NEOM Saudi Arabia

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

This paper proposes an unmanned aerial vehicle (UAV)-enabled wake-up radio (WuR) and data collection strategy for an Internet of Things (IoT) network. The considered model involves employing UAVs to awaken IoT devices from an ultralow power sleep mode through the transmission of WuR signals. Diverging from traditional methods where all IoT devices engage in data collection post-wake-up, the proposed framework centers on a more streamlined strategy. In the system model, a single UAV continuously traverses a specific region without hovering for the sake of waking-up nearby IoT devices. Specifically, solely IoT devices identifying valuable or unusual information transmit data back to the UAV upon a successful wake-up, which we refer to as opportunistic sensing. Hence, for reliable communication, it becomes important to determine the optimal height and velocity of the UAV while traversing the IoT region so as to maximize the probability that the UAV successfully wakes-up enough IoT devices to cover the field, followed by successful data collection of valuable sensed information. To this end, the paper proposes a low-complexity, fast in convergence, simple to implement efficient method to solve the non-convex optimization problem. The proposed algorithm relies on multidimensional bisection method, and is specifically tailored to determine the UAV's velocity and height, along with optimizing the device density. The numerical results in the paper validate the superior performance of the proposed algorithm when compared to conventional baselines.

关键词： Performance evaluation signal processing algorithms Data collection signal processing Autonomous aerial vehicles Sensors Internet of Things Reliability Optimization Convergence

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Zero-Bit Transmission of Adaptive Pre- and De-Emphasis Filters for Speech and Audio Coding

Zero-Bit Transmission of Adaptive Pre- and De-Emphasis Filte...

引用

International Conference on Telecommunications and signal processing (TSP)

作者： Niloofar Omidi Piralideh Philippe Gournay Roch Lefebvre Department of Electrical and Computer Engineering University of Sherbrooke Quebec Canada

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This paper introduces a novel adaptation approach for first-order pre- and de-emphasis filters, an essential tool in many speech and audio codecs to increase coding efficiency and perceived quality. The proposed zero-bit self-adaptation approach differs from classical forward and backward adaptation approaches in that the de-emphasis coefficient is estimated at the receiver, from the decoded pre-emphasized signal. This eliminates the need to transmit information that arises from forward adaptation as well as the signal-filter lag that is in-herent in backward adaptation. Evaluation results show that the de-emphasis coefficient can be estimated accurately from the decoded pre-emphasized signal and that the proposed zero-bit self-adaptation approach provides comparable subjective improvement to forward adaptation.

关键词： Filters Codecs Speech coding Costing Bit rate signal processing algorithms Receivers

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An Improved RT-DETR Algorithm for Small Object Detection in Aerial Images

An Improved RT-DETR Algorithm for Small Object Detection in ...

引用

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

作者： Zhongzhi Zheng Yuan Jia Southwest University of Science and Technology Mianyang Sichuan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

To address the issues of low detection accuracy in drone aerial images due to small object sizes, indistinct features, complex backgrounds, and significant variations in object scale, this paper proposes an improved small object detection algorithm for drone aerial images: RT-DETR (Real-Time Detection Transformer). First, a lightweight multi-scale feature extraction module (CF-Block) is introduced into the backbone network, where the FasterNet Block is enhanced with convolutional gated linear units to improve the extraction of small object features and reduce computational redundancy. Second, a lightweight feature fusion mechanism, SOEP (Small Object Enhance Pyramid), is designed to integrate additional shallow feature maps, enhancing small object feature information, while the Omni-Kernel Module (OKM) is employed to fully fuse multi-scale feature information. Finally, the loss function is optimized with the proposed W-ShapeIoU, which maintains scale invariance while adopting a gradient gain distribution strategy to suppress harmful gradi-ents generated by low-quality anchor boxes, thereby improving the detector's overall performance. Experiments on the public Visdrone2019 dataset show that the improved algorithm reduces the parameter count by 18.8 % compared to the baseline model, while increasing mAP@0.5 and mAP@0.5:0.95 by 2.6% and 1.9 %, respectively. Compared to current mainstream algorithms, the proposed method achieves an accuracy improvement of more than 8 %, demonstrating superior detection performance.

关键词： Adaptation models Accuracy Computational modeling Biological system modeling signal processing algorithms Feature extraction Data models Computational efficiency Drones Load modeling

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Localization Without GNSS for TDMA-Based Multi-Hop Networks

Localization Without GNSS for TDMA-Based Multi-Hop Networks

引用

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

作者： Ege Orkun Gamgam Tolga Numanoglu ASELSAN Inc. Ankara Turkey

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

A growing number of military systems require accurate positioning and awareness of their surroundings to operate effectively. A widely used technology for localization is Global Navigation Satellite Systems (GNSS) including Global Positioning System (GPS). However, GNSS-enabled applications are prone to spoofing attacks as well as jamming attacks due to low levels of received power. In this study, we develop an alternative localization solution for TDMA-based multi-hop networks by taking advantage of already available network synchronization and topology information. First, we comparatively study two multidimensional scaling (MDS)-based positioning techniques, namely Classical MDS (CMDS) and Scaling by Majorizing a Complicated Function (SMACOF), in terms of localization error under different scenarios with varying system parameters. Moreover, we discuss these two techniques in terms of the applicability to multi-hop networks as well as the vulnerability against local minimums during optimization. Obtained results from this analysis are then used to develop the proposed localization solution, namely Iterative-MDS (I-MDS), which leverages both the SMACOF and CMDS algorithms as well as BronKerbosch algorithm. Through numerical examples, we evaluate the effectiveness of I-MDS algorithm in both low and high density multi-hop networks without any prior information on node locations.

关键词： Location awareness Global navigation satellite system signal processing algorithms Spread spectrum communication signal processing Topology Performance analysis Synchronization Reliability Global Positioning System

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A Ginkgo Detection Algorithm in Complex Environments Based on Improved YOLOv7

A Ginkgo Detection Algorithm in Complex Environments Based o...

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Chi Ma Qiang Guo School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Detections of Ginkgoes are prerequisites for later counting and harvesting. Due to the uneven distribution of samples, the detection speed and accuracy of existing algorithms cannot adapt to the impact of complex environments. Therefore, an enhanced model YOLOv7-DC based on YOLOv7 is proposed in this paper, which redesigns the detection network and introduces a new feature fusion method. DCNv2 is embedded in the efficient layer aggregation network (ELAN), while PConv is utilized instead of conventional convolution to reduce the parameter impact of DCNv2. Moreover, the attention mechanism CBAM is introduced during training to enhance spatial and channel information, and the ConvMixer architecture is employed to capture spatial and channel relationships within the features, which are transmitted to the detection head through attention mechanism, improving the model's detection accuracy for each specific classification sample. Experimental results show that our YOLOv7-DC achieves both excellent detection speed and recognition rate in various classification tasks. The improved model's average detection accuracy is increased by 6.2% compared to previous algorithms, and the model parameters are reduced by 13%. It is proved that YOLOv7-DC is more suitable for scenarios with imbalanced samples and complex environments.

关键词： Training Accuracy Attention mechanisms Convolution signal processing algorithms Lighting Medical services Feature extraction Environmental factors Detection algorithms

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Evaluating Public Sentiments about Turkish Universities through Twitter Using Deep Learning Techniques

Evaluating Public Sentiments about Turkish Universities thro...

引用

IEEE signal processing and Communications Applications (SIU)

作者： Abdulfattah E. Ba Alawi Ferhat Bozkurt Bilgisayar Mühendisliği Bölümü Mühendislik Fakültesi Atatürk Üniversitesi Erzurum Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Evaluating public attitudes towards Turkish institutions is critical for successful strategic decision-making and reputation management in today’s dynamic educational settings. To tackle this problem, this study examines sentiment analysis of Twitter data to assess public impressions of Turkish institutions using a variety of deep learning algorithms, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and a hybrid model. Using a large dataset from Twitter, our technique focuses on interpreting the attitudes expressed about Turkish universities. After pre-processing the data, the suggested models are rigorously trained and tested, achieving an impressive 87.25% accuracy rate. The results give insights into popular attitudes and provide a comparative study of the models used. The findings illustrate the efficiency of deep learning approaches in identifying attitudes about Turkish institutions on Twitter, providing significant insights for handling reputation and decision-making in the educational field.

关键词： Deep learning Sentiment analysis Social networking (online) Heuristic algorithms Blogs Decision making signal processing algorithms

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Multistatic MIMO Radar Tensor Tracking Based on Coupled Canonical Polyadic Decomposition

Multistatic MIMO Radar Tensor Tracking Based on Coupled Cano...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Dong Zhao Xiao-Feng Gong Lei Wang Li-Na Luo School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Coupled canonical polyadic decomposition (C-CPD) can be used to represent a multistatic MIMO radar having a single transmit array and multiple receive arrays. Most of the existing algorithms of C-CPD model focus on the situation where the targets are stationary and they are limited to batch algorithms. However, in practice, each target may be moving, which makes the model dynamic. Therefore, the corresponding tracking algorithms are in urgent need. In this paper, we propose a C-CPD tracking method based on simultaneous diagonalization (SD) algorithm for multistatic MIMO radar. Theoretical and experimental results are provided to illustrate the advantages of the proposed algorithm compared with the batch C-CPD algorithms and CPD tracking methods.

关键词： Employee welfare Target tracking Tensors Accuracy Heuristic algorithms MIMO radar signal processing algorithms signal processing Radar tracking Complexity theory

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Research on Penetration Testing Path Planning Method Based on Improved Potential Field Ant Colony Algorithm

Research on Penetration Testing Path Planning Method Based o...

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International Conference on signal processing Proceedings (ICSP)

作者： Zhendong Qin Rui Liu Chenyan Sun Xiao Jin Rui Luo China Aerospace Academy of Systems Science and Engineering Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

This study proposes a penetration testing path planning method based on an improved potential field ant colony algorithm. First, the network topology is modeled as a graph structure, with each communication link assigned an attack cost weight. To overcome the issue of traditional artificial potential fields easily falling into local optima, this paper introduces a repulsion function that dynamically adjusts the repulsion intensity, weakening it as nodes approach the target system, thereby enhancing global search capability. The ant colony optimization algorithm is then employed, which iteratively optimizes path selection by simulating the pheromone release mechanism of ants. By combining the local search capability of the artificial potential field with the global search capability of the ant colony algorithm, the proposed improved algorithm demonstrates superior performance in terms of computation time, convergence speed, and path planning success rate. Experimental results in different network environments show that the improved algorithm’s performance increasingly surpasses that of other algorithms as network size grows, offering higher path planning success rates and faster convergence speeds. This study provides an efficient and reliable method for path planning in network security penetration testing.

关键词： Ant colony optimization Network topology Heuristic algorithms signal processing algorithms signal processing Network security Path planning Reliability Convergence Penetration testing

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Estimating Vehicle Length Using FMCW Radar: A Micro-Doppler Signature Analysis Approach

Estimating Vehicle Length Using FMCW Radar: A Micro-Doppler ...

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European signal processing Conference (EUSIPCO)

作者： Victor R.J. Deville Jamie S. Evans Jonathan H. Manton Department of Electrical and Electronic Engineering The University of Melbourne Victoria Australia

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Effective traffic management and safety in Intelligent Transportation Systems (ITS) significantly benefit from various data points, with vehicle length being one important aspect. Current radar-based methods often fall short of demonstrating the capability to assess vehicle lengths in diverse traffic conditions. To bridge this gap, we introduce a novel algorithm employing time-frequency analysis of radar signals at a fixed range bin, enabling the estimation of the time vehicles of varying lengths and speeds take to cross this range bin. Assuming known vehicle velocities, this method allows the estimation of vehicle length. We develop a signal model of the vehicle's bulk motion extracted from its micro-Doppler signature. We then introduce three duration estimation methods: Maximum Likelihood Estimation (MLE) assuming Gaussian noise and two heuristic approaches utilising threshold calculations. Through testing on synthetic data and further validation using both simulated and real radar data, we evaluate the accuracy and computational efficiency of these methods. The results underscore the potential of our time-frequency analysis approach for vehicle length estimation, highlighting the prospect of non-invasive radar-based data collection systems in ITS.

关键词： Maximum likelihood estimation Time-frequency analysis Accuracy Gaussian noise signal processing algorithms Radar Data collection Standards Testing Synthetic data

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