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检索条件"主题词=Signal Processing Algorithms"

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Female Mosquito Detection by Means of AI Techniques Inside Release Containers in the Context of a Sterile Insect Technique Program

Female Mosquito Detection by Means of AI Techniques Inside R...

引用

European signal processing Conference (EUSIPCO)

作者： Javier Naranjo-Alcazar Jordi Grau-Haro David Almenar Pedro Zuccarello Instituto Tecnológico de Informática (ITI) Valencia Spain Empresa de Transformación Agraria S.A. S.M.E. M.P. (TRAGSA) Paterna Spain

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

The Sterile Insect Technique (SIT) is a biological pest control technique based on the release into the environment of sterile males of the insect species whose population is to be controlled. The entire SIT process involves mass-rearing within a biofactory, sorting of the specimens by sex, sterilization, and subsequent release of the sterile males into the environment. The reason for avoiding the release of female specimens is because, unlike males, females bite, with the subsequent risk of disease transmission. In the case of Aedes mosquito biofactories for SIT, the key point of the whole process is sex separation. This process is nowadays performed by a combination of mechanical devices and AI-based vision systems. However, there is still a possibility of false negatives, so a last stage of verification is necessary before releasing them into the environment. It is known that the sound produced by the flapping of adult male mosquitoes is different from that produced by females, so this feature can be used to detect the presence of females in containers before environmental release. This paper presents a study for the detection of females in Aedes mosquito release vessels for SIT programs. A particular set-up was developed to provoke the flight of mosquitoes inside the containers and record the sound produced by this flight. Due to the difficulty in identifying the exact timing of female flight within mixed male/female containers an anomaly detection strategy was chosen to be followed rather than a multi-class classification strategy. Two algorithms were implemented for the detection of female mosquitoes: an unsupervised outlier detection algorithm (iForest) and a one-class SVM trained with male-only recordings. The experiments conducted show that the results are highly dependent on the degree of maturity of the mosquitoes. For the detection of females with a 25/75% female/male ratio, the best results are obtained on the seventh day after sexing in the pupal stage

关键词： Performance evaluation Accuracy Insects Machine vision signal processing algorithms Containers Recording Classification algorithms Artificial intelligence Anomaly detection

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Research on Cleaning Method of Wind Power Data Based on Improved Quartile Method

Research on Cleaning Method of Wind Power Data Based on Impr...

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Qixue Guan Keying Geng Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

A method is proposed to address the challenge of anomalous wind speed and power data in wind farms, aiming to clean the wind power data. This method utilizes an enhanced quartile approach and involves two main steps. Initially, the Bin zoning method is employed to preprocess the extensive data by leveraging the actual operational data from wind turbines in Jilin Wind Farm. Subsequently, the enhanced quartile method is implemented to cleanse the wind power data post zoning. In this step, the improved quartile method cleans the scattered data, and the MeanShift algorithm is employed to clean the folded data. Finally, the proposed algorithm is compared with the data purified by the quartile method and the MeanShift method, respectively.

关键词： Performance evaluation Correlation Wind speed Data preprocessing signal processing algorithms Wind power generation Wind farms signal processing Cleaning Wind turbines

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Network Provisioning in SDN: Optimizing Network Resources for Enhanced Performance

Network Provisioning in SDN: Optimizing Network Resources fo...

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

作者： G D Nithin D Yathis Kumar S. Nithin K S Sree Hari Nathan R Gandhiraj Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

Software defined Networking (SDN) provides a comprehensive view of the network and is good at handling changes in a traditional and simple network without needing a lot of updates. The purpose of this study was to determine the performance of SDN in various scenarios. However, the usual SDN architecture faced problems in handling bottlenecks. In our early research, we referred a lot of research papers for creating a model for a regular wired network with less nodes and traffic using standard SDN architecture. We developed the model using NetSim and observed the various network parameters such as overall throughput, delay and jitter. The recorded throughput exhibited a decrease, accompanied by increased delay and jitter, when assessing this basic network configuration. Later, we propose a new model with improved SDN performance, which resembles a normal wireless IoT network in real-time. The proposed model contains a novel priority and congestion control mechanism, called as ‘Updated SDN’, which has worked well in reducing network congestion especially in practical situations. It also maintains network's quality of service. The model provided results which had a significant improvement of 2% from the previous model. In Overall, our model looks at network delays and congestion at the SDN controller and delivers the packet properly with minimized loss. The results were analyzed by comparing the results of updated SDN and the standard SDN.

关键词： Wireless communication Performance evaluation signal processing algorithms Computer architecture Jitter Throughput Delays

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VLSI Architecture for the Phase Unwrapping Module in Contactless Vibration Sensing for Biomedical Applications

VLSI Architecture for the Phase Unwrapping Module in Contact...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Mujeev Khan Ashi Singhal Mohd Wajid Abhishek Srivastava Department of Electronics Engineering Aligarh Muslim University Aligarh India Centre for VLSI and Embedded Systems Technology IIIT Hyderabad Hyderabad India

ISBN: (数字)9798331533229

ISBN: (纸本)9798331533236

Phase-unwrapping (PU) is a critical signal processing step used in fields such as radar, sonar, and laser technology. This paper presents a faster and more efficient VLSI architecture for implementing the phase-unwrapping algorithm on FPGA platforms. To evaluate the effectiveness of the proposed FPGA-based VLSI architecture for the phase-unwrapping algorithm in chest wall vibration sensing, we compared its performance with both a PYNQ board implementation and an equivalent software solution. The results are compared in terms of speed, resource utilization, and power consumption. Additionally, prototypes were developed on both the ZedBoard and PYNQ board, and the outcomes were physically verified. The proposed VLSI architecture achieves nearly 70 x and 111 x speed-up compared to software-based solutions and PYNQ board implementations, respectively.

关键词： Vibrations Power demand Laser radar Software algorithms signal processing algorithms Computer architecture Very large scale integration Software Sensors Field programmable gate arrays

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Advancements in Wireless Networks: Transition from 5G to Beyond

Advancements in Wireless Networks: Transition from 5G to Bey...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Gunasekaran Thangavel C. Malarvizhi M. Ameena Banu Arun Anthonisamy M. Hemalatha Raja Manikandan Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Sultanate of Oman Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chembarambakkam India Department of Electronics and Communication Engineering NPR College of Engineering and Technology Natham India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamil Nadu India Department of Information Technology Nandha Arts and Science College Erode Tamil Nadu India Department of ECE K. Ramakrishnan College of Technology Trichy Tamil Nadu India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies’ benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology’s effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks.

关键词： Technological innovation 5G mobile communication Channel capacity Spectral efficiency Array signal processing Wireless networks signal processing algorithms Throughput Millimeter wave communication Ultra-dense networks

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A Indian Rainfall Prediction Using Machine Learning algorithms: A Comparative Study

A Indian Rainfall Prediction Using Machine Learning Algorith...

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： Anuradha R Arjun Sudheer Anvin P Shibu Dulal Roy Mohan Krishna G R Deepak Vishal J Abhijith K Department of CSE SREC Coimbatore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

This study is intended to make rain forecasts more accurate so that there can be early warnings and measures against rain-caused disasters. We do this by using sophisticated computer programs which take into account many things like the air, the ocean, and land. In order to come up with the most precise model for predicting when it will rain heavily or not at all, we have to prioritize what data should be used first; select different methods which includes Support Vector Machines, Decision Trees, Random Forests among others while working on feature extraction through machine learning algorithms like Naive Bayes Classifier or Logistic Regression. We want our systems to always predict right - never wrong about whether there's going to be a lot of rainfall tomorrow. Our approach optimizes over daily IMD Indian Meteorological Department rainfall records from website ***.

关键词： Rain Machine learning algorithms Accuracy Disasters Weather forecasting signal processing algorithms Sea measurements

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Cell-Free MIMO in 6G NTN with AI-predicted CSI

Cell-Free MIMO in 6G NTN with AI-predicted CSI

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IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Bruno De Filippo Riccardo Campana Alessandro Guidotti Carla Amatetti Alessandro Vanelli-Coralli Dept. of Electrical Electronic and Information Engineering (DEI) Univ. of Bologna Bologna Italy National Inter-University Consortium for Telecommunications (CNIT) Italy

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Non-Terrestrial Networks (NTNs) in 6G are expected to integrate the Terrestrial Networks (TNs) coverage and provide connectivity to a multitude of User Terminals (UTs). In order to cope with the traffic demand, NTNs can employ cell- free MIMO with full frequency reuse schemes to maximize the spectral efficiency of the system. However, such technique can be strongly affected by channel aging, impacting their application to Low Earth Orbit (LEO)-based NTNs. Aiming at counteracting this effect, we present in this paper a lightweight Artificial Intelligence (AI) model for Channel State Information (CSI) prediction. To predict the propagation channel, the proposed algorithm learns its temporal statistics, e.g., the Line-of-Sight (LoS) shadowing correlation, from data. The model then applies the corrections to the feedback CSI, minimizing the difference with respect to the propagation channel encountered at transmission time. System-level analyses on a LEO-based CF-MIMO system report an improvement of the per-user capacity by up to 15% and a reduction of the outage probability when predicted CSI are used instead of aged channel coefficients.

关键词： 6G mobile communication Wireless communication Spectral efficiency signal processing algorithms Probability Aging Numerical models Power system reliability Artificial intelligence Capacity planning

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A Study of EEG signal Classification in Alzheimer's Disease Using the IMPSO for Channel Selection

A Study of EEG Signal Classification in Alzheimer's Disease ...

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

作者： Ruofan Wang Haojie Xu Deri Yi Changzhi Song Jitong Wang School of Information Technology Engineering Tianjin University of Technology and Education

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Alzheimer's disease (AD) is a neurodegenerative disease that is primarily characterised by insidious onset and subtle clinical symptoms, which can make it challenging to diagnose through unconventional means. EEG has been extensively employed as a primary diagnostic tool for Alzheimer's disease (AD), facilitating the assessment and diagnosis of various neurological disorders. An accurate diagnosis is crucial for the prevention of the transition from early cognitive impairment to Alzheimer's disease (AD) and the provision of early treatment for AD patients. The maj ority of classification methods concentrate solely on the time or frequency domain, thereby neglecting the interconnectivity between the two. Accordingly, this paper generates time- frequency spectrograms based on a wavelet transform for analysis. However, the time-frequency spectrograms contain a considerable amount of redundant information, which results in a sudden increase in computation. To address this issue, this paper proposes a bidirectional principal component analysis (BDPCA) method to reduce the dimensionality of the time- frequency spectrogram, thereby reducing the computational burden while effectively restoring the features of the image. Subsequently, the improved PSO algorithm was employed to select the 16 channels, and seven key channels (Fp1, F7, F8, F3, T3, T4 and 01) were ultimately identified. The features of these selected channels were then inputted into the XGBoost classifier to achieve the recognition of patients and normal controls, with an accuracy rate of 93.56%. This study is the inaugural application of the IMPSO optimisation algorithm and time- frequency spectrogram to EEG channel selection, thereby establishing an efficient framework for AD detection. The method reduces the computational complexity and improves the generalisation ability, which facilitates the rapid identification and reduction of misdiagnosis in a clinical setting while also advancing the understanding of t

关键词： Wavelet transforms Neurological diseases Time-frequency analysis Accuracy signal processing algorithms Electroencephalography Classification algorithms Alzheimer's disease Spectrogram Optimization

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An enhanced YOLOv8 and its application for risk prevention in underground excavation operations

An enhanced YOLOv8 and its application for risk prevention i...

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International Conference on signal processing Proceedings (ICSP)

作者： Tianzheng Liu Kefei Li Yuyuan Pu Houxi Zhu Jianfeng Yi Beijing MTR Construction Administration Company Limited Beijing China Beijing Key Laboratory for Urban Rail Transit Automated Operation System and Safety Monitoring Beijing China Beijing Infrastructure Investment Company Limited Beijing China Beijing Metro Consultancy Corporation Limited Beijing China National Engineering Laboratory for Urban Rail Transit System Safety Assurance Technology Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Small target detection is used in underground underground excavation operations. At present, there are still challenges and problems in small target detection. Among them, small targets have low resolution in target detection, have few features that can be extracted, and are susceptible to environmental noise interference. In addition, it is difficult to accurately extract features for targets that span multiple local areas and have odd shapes. In response to these problems, this paper proposes an enhanced YOLOv8 object detection algorithm for safety precautions in underground excavation operations. Among them, a new multi-scale feature fusion module is proposed, and the original P2 layer of the benchmark model is added to the feature pyramid of the neck network for feature fusion to improve the detection accuracy of small targets. The Global Attention Mechanism is added to the backbone network of the benchmark model so that the model can focus on more important key features. In addition, it is proposed to introduce the deformable convolution DCNv3 into the benchmark model structure to replace the original fixed convolution kernel, making the convolution operation more flexible and adaptable, and increasing the receptive field required for the detection task. The improved algorithm improves the detection effect of small targets on the COCO dataset, and the algorithm is applied to the dark mining scenario, and it is also improved on the dark mining dataset. The experimental results prove the effectiveness of this method.

关键词： Adaptation models Attention mechanisms Convolution Working environment noise Prevention and mitigation signal processing algorithms Object detection Benchmark testing Feature extraction Excavation

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MMV based EMG signal Reconstruction for Remote Rehabilitation

MMV based EMG Signal Reconstruction for Remote Rehabilitatio...

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IEEE International Conference on Robotics and Biomimetics

作者： Ruihui Zhao Haoxiang Zhang Yehao Ma Robotics Institute Ningbo University of Technology Ningbo China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

To overcome the limitations of low compression rates and extended reconstruction times in the Single Measurement Vector (SMV) model for electromyography (EMG) signal processing, this paper presents a compression and reconstruction algorithm based on the Multiple Measurement Vector (MMV) model. The EMG signals undergo preprocessing, and the optimal combination of the Discrete Sine Transform (DST) sparse basis and the BPBDMtx measurement matrix is selected. signal reconstruction is then performed using the Orthogonal Matching Pursuit (MMV-OMP) algorithm. Experimental results show that the proposed MMV-based compression sensing algorithm significantly outperforms the traditional SMV model in terms of reconstruction quality, compression rate, and reconstruction speed. This method offers a novel pathway for efficient EMG signal reconstruction and has broad potential for applications in sensor technology and remote rehabilitation.

关键词： Telemedicine Matching pursuit algorithms signal processing algorithms Transforms Reconstruction algorithms Robot sensing systems Electromyography Vectors signal reconstruction Sparse matrices

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