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Power Allocation for Non-Coherent Multi-Carrier FSK Underwater Acoustic communication systems with Uneven Transmission Source Level

Power Allocation for Non-Coherent Multi-Carrier FSK Underwat...

引用

international conference on Signal Processing systems, ICSPS

作者： Yunfei Zhao Lei Wan Yougan Chen En Cheng Feng Xu Lei Liang Dept. of Information and Communication Engineering Xiamen University Xiamen China Dept. of Applied Marine Physics and Engineering Xiamen University Xiamen China Wuhan Second Ship Design and Research Institute Wuhan China

Non-coherent multi-carrier Frequency Shift Keying (FSK) is widely used in underwater acoustic communication systems due to its high reliability, low receiving complexity, and less susceptibility to channel conditions. In practical underwater acoustic communication systems, due to the wide band nature of the signal, the overall transmitting source level is usually not flat. Targeting at this issue, this paper proposes power allocation methods based on genetic algorithm and adaptive greedy algorithm, which maximize channel capacity and system robustness. Results based on simulated and pool experimental channels show that our methods can achieve higher channel capacity and lower bit error rate than power averaging scheme, therefore better system performance can be obtained.

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PAPR Suppression for OFDM Integrated Radar and communication System Based on PTS-Improved Clipping

PAPR Suppression for OFDM Integrated Radar and Communication...

引用

international conference on Signal Processing systems, ICSPS

作者： Yao Song Biao Jin Ocean College Jiangsu University of Science and Technology Zhenjiang Jiangsu China

Orthogonal Frequency Division Multiplexing (OFDM) is one of the commonly used waveforms of integrated radar and communication systems, but it suffers from some disadvantages, such as the high peak-to-average power ratio (PAPR). In order to suppress the PAPR of OFDM, we combine the partial transmit sequence (PTS) method with the improved clipping method. First, we divided the subcarriers into several groups by the partial transmission sequence method. then each group of subsequences is multiplied by the corresponding phase factor, and the smallest PAPR in subsequences groups is selected. Finally, the PAPR of OFDM integrated radar and communication system reduced furtherly by multiple amplitude limiting iterative filtering. Simulation experiments show that the proposed method can reduce the PAPR by 2.3dB. the ambiguity function changes little after PAPR suppression, and it has little impact on the radar detection performance.

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Deep-Prenatal: A Deep Learning Approach for Detection and Analysis of Nuchal Translucency for Enhanced Prenatal Screening

Deep-Prenatal: A Deep Learning Approach for Detection and An...

引用

international conference on communication systems and Network Technologies (CSNT)

作者： Smita S. Pawar Mangesh D. Nikose Anni U. Gupta Electrical and Electronics Engg. Dept School of Engg and Technology Sandip University Nashik Maharashtra India Electronics and Communication Engg. Dept. MET's IOE Nashik Maharashtrax India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nuchal Translucency (NT) observed in ultrasound scans is widely utilized to identify genetic abnormalities in foetuses. However, NT measurements may be challenging to perform as a mainstay of prenatal screening. these challenges include operator dependency, which primarily relies on the technician’s expertise, and variations in imaging settings that can introduce data anomalies. thus, the precision of NT measurements is not always definitive, raising questions about their dependability in clinical practice. A Convolutional Neural Network (CNN) framework is proposed to address these constraints. this architecture comprises of Conv2D, Max Pooling, Dense, and Flatten layers and advanced components. Together, these elements enable the CNN to effectively identify and analyse NT measurements, thereby automating the process and reducing reliance on human involvement. the proposed CNN architecture performs well across critical criteria such as accuracy, precision, recall, and Intersection over Union (IoU). Furthermore, the framework has demonstrated stability when applied to diverse datasets, owing to its robustness and flexibility across various clinical databases. this proposed efficient and reliable preliminary screening method, based on the enhanced CNN architecture, addresses the inherent limitations of NT measurements. It establishes a new benchmark for accuracy in the field and introduces a transformative approach that enhances clinical outcomes.

关键词： Location awareness Accuracy Ultrasonic imaging Ultrasonic variables measurement Stability criteria Imaging Benchmark testing Genetics Robustness Convolutional neural networks

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the Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks

The Validity of Using Technical Indicators When forecasting ...

引用

international conference on Computational Intelligence and communication networks (CICN)

作者： Salahadin Mohammed Department of Information and Computer Science College of Computing and Mathematics King Fahd University of Petroleum and Minerals Dhahran Kingdom of Saudi Arabia

ISBN: (纸本)9781665487726

Many researchers use deep learning and technical indicators to forecast future stock prices. there are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. the aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. the experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. they also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.

关键词： Deep learning Solid modeling Predictive models communication networks Forecasting Computational intelligence

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New Approach in the Error Performance Analysis of SM over Time-Variant Rayleigh Fading Channels

New Approach in the Error Performance Analysis of SM over Ti...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Yazid M. Khattabi Salim A. Alkhawaldeh Department of Electrical Engineering The University of Jordan Amman Jordan Communication Engineering Department Albalqa Applied University Amman Jordan

ISBN: (纸本)9781665487726

In this paper, a spatial modulation (SM)-based multiple input multiple output (MIMO) wireless communication system that is operating over time-variant Rayleigh fading channels is considered. the channel state information (CSI) is assumed to be estimated perfectly and on the basis of the piloted estimation method. For such a system, an analytical approach is proposed to derive the system”s conditional pairwise-error-probability (PEP) exactly. the approach starts by reducing the SM maximum likelihood (ML) decoding rule to classical decision-statistic rule. the distribution of the decision-statistic is then determined to be zero-mean Gaussian, which helps in obtaining the conditional PEP directly in terms of the Q-function. the derived conditional PEP expression is new, explicitly expressed in terms of variant system and channel parameters, and directly used to compute the system”s average bit error probability. Numerical and simulation results are presented to verify the derivations and to demonstrate some insightful performance observations.

关键词： Fading channels Wireless communication Maximum likelihood estimation Error probability Simulation Computational modeling Modulation

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Performance and Efficiency Analysis for Lithium-ion Battery Using State of Charge Method

Performance and Efficiency Analysis for Lithium-ion Battery ...

引用

international conference on Computational Intelligence and communication networks (CICN)

作者： Walter Udeze Sarhan M. Musa Department of Electrical and Computer Engineering Prairie View A&M University Prairie View TX USA

ISBN: (纸本)9781665487726

Companies throughout the world are making an innovative switch from oil and gas to renewable energy sources, such as wind and solar power. As the world transitions to renewable energy, the demand for electric vehicles (EVs) has increased significantly. EVs mainly use Lithium-ion batteries because of their durability and efficiency. However, as the number of Lithium-ion batteries increases with the goal of reduction of emission and low energy cost, it comes with a major drawback of safety which affects efficiency. To address these challenges, this study investigates ways on how to improve the storage management system in an EV. In this research, different Lithium-ion battery states of an EV were monitored to effectively improve the battery management system (BMS). Two different drive cycles, federal test procedure (FTP) 75 and wide-open throttle (WOT) simulation time of 2474 seconds are used to obtain the results. the implementation of State of charge (SOC) technique has been applied to evaluate the energy remaining in the battery as well as the aging effects/dynamic load. the results obtained show a rate that gradually slows down in a linear manner. In addition, EVs have a less likely chance of experiencing power loss due to a very sophisticated gear system and it is very similar to a hybrid electric vehicle or internal combustion engine.

关键词： Lithium-ion batteries Renewable energy sources Oils Storage management Battery management systems Voltage Switches

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Power Coverage Optimization in Cellular Radio networks Using Neural Architecture Search

Power Coverage Optimization in Cellular Radio Networks Using...

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international conference on Wireless communications and Signal Processing (WCSP)

作者： Juan Zhu Tianyu Wang Zhe Xiao Shaowei Wang School of Electronic Science and Engineering Nanjing University Nanjing China Science and Technology on Communication Networks Laboratory Shijiazhuang China

ISBN: (纸本)9781665450867

Due to the complex propagation environment, it is difficult to find the optimal antenna configuration that ensures the best coverage of cellular networks. Most existing work adopts heuristic methods, where the antenna configuration is evaluated and updated by calculating the coverage of the entire area iteratively. However, in real networks with a large number of antennas and highly complex ray-tracing models, the heavy computational burden caused by repetitive coverage calculation is usually unacceptable. In this paper, we propose a deep learning approach that straightly infers the antenna configuration from the raw data given by the city map. Specifically, we adopt the emergent neural architecture search to automatically design a specific neural network for the considered power coverage optimization problem. Numerical results show that the discovered neural architecture greatly outperforms the conventional hand-crafted architectures.

关键词： Deep learning Wireless communication Design methodology Urban areas Neural networks Computer architecture Signal processing

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Defect Reconstruction for Class-imbalanced Power System Defect Recognition

Defect Reconstruction for Class-imbalanced Power System Defe...

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international conference on Wireless communications and Signal Processing (WCSP)

作者： Hongxing Wang Mei Wu Yu Song Xin Zhang Weiping Mao Jiangsu Frontier Electric Power Technology Company Nanjing China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665450867

Performing fault diagnosis is an important routine to keep power systems functioning properly. Since most facilities of power systems are located in the wild, unmanned aerial vehicles (UAV) are used to collect potentially damaged components of power systems by taking pictures. those pictures are categorized into a certain type to take corresponding actions to repair the damaged components. It is vital to classify collected images accurately. However, the collected images distribute in a class-imbalanced style, which degrades the performance of the classifier if directly used for training. In this paper, we make use of the generative adversarial network (GAN) to generate extra images for classes that have fewer images. Our method achieves decent improvements on 4 different scenes, showing the effectiveness of GAN-generated images on the class-imbalanced power system defect classification task.

关键词： Wireless communication Training Fault diagnosis Signal processing Maintenance engineering Generative adversarial networks Autonomous aerial vehicles

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Classification of Aglaonema Using Machine Learning

Classification of Aglaonema Using Machine Learning

引用

international conference on Computational Intelligence and communication networks (CICN)

作者： Agus Pratondo Astri Novianty Dept. of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Dept. of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9781665487726

Recently, Aglaonema has become an ornamental plant with high economic value. Various Aglaonema variants are available in the market with various prices offered, e.g., Lipstick, Suksom, Red cochin, Widuri, and Pride of Sumatra (pos). People often need help distinguishing the variants to prevent being deceived when buying the plants. A classifier to distinguish several local Aglaonema variants with high similarities is proposed to solve this problem. A collection of images from each Aglaonema variant are used to train the classifier. Both traditional classification algorithms, such as the $k$ -nearest neighbours and support vector machines, and modern algorithms, specifically the Inception-v3, were employed. Classifier evaluation is performed by applying 5-folds cross-validation. Experimental results show that the best level of accuracy is obtained from the Inception-v3 algorithm by achieving an accuracy rate of 83.02%. this result indicates that the classification of Aglaonema is relatively hard, but feasible to continue for improvements.

关键词： Support vector machines Economics Deep learning Machine learning algorithms Classification algorithms communication networks Computational intelligence

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Optimization for Small Wireless communication networks by Scenario Tracking 14

Optimization for Small Wireless Communication Networks by Sc...

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14th Annual IEEE international systems conference (SysCon)

作者： Liu, Xian Univ Arkansas Dept Syst Engn Little Rock AR 72204 USA

ISBN: (纸本)9781728153650

For wireless networks, it is highly attractive to design a QoS-aware strategy to manage the transmission power. In this paper, we present a fading immunization scheme based on the stochastic programming methodology. this scheme is applied to small autonomous wireless networks and incorporates a scenario tracking procedure to conduct QoS-aware optimization.

关键词： communication systems energy optimization Quality of Service scenario tracking

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