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Non-linear Filtering Techniques for Improving Accurate Vehicle Angle Determination

Non-linear Filtering Techniques for Improving Accurate Vehic...

引用

Kleinheubach Conference

作者： Richard Pfleiderer Jannes Ramsdorf Yannis Boländer Holger Blume Institute of Microelectronic Systems Leibniz University Hannover Hannover Germany

ISBN: (数字)9783948571122

ISBN: (纸本)9798331541774

Accurate measurement of vehicle angles, particularly pitch angles, is crucial for the safety and efficiency of modern vehicles. These angles affect vehicle dynamics such as acceleration and braking and are essential for adaptive headlight systems that adjust lighting based on vehicle pitch. Various methods exist for measuring vehicle pitch angles, including LiDAR, wheel sensors combined with inclinometers, and inertial measurement units or accelerometers in conjunction with wheel sensors. A cost-effective approach employs an algorithm that calculates real acceleration from vehicle speed and accounts for road inclination using only accelerometers. However, this algorithm only provides reliable results with accurate accelerometer data. Since accelerometers are prone to noise, signal processing requires filtering. Linear filters, which significantly smooth the signal, are unsuitable here as they can distort crucial signal information through over-smoothing. Instead, nonlinear filters offer a better solution. The Kuwahara filter, total variation filter, and bilateral filter are such nonlinear alternatives that reduce noise while preserving essential signal details, such as edges and features. Tests indicate that without adequate filtering, the average deviation in pitch angle is 1.4 degrees, with a maximum error of 19.4 degrees. With the use of the bilateral filter, this average error is reduced to 0.11 degrees and the maximum error to 1.2 degrees.

关键词： Accelerometers Accuracy Roads Noise Wheels signal processing algorithms Nonlinear filters Sensors Safety Vehicle dynamics

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Machine Learning-Based Analysis and Prediction of Liver Cirrhosis

Machine Learning-Based Analysis and Prediction of Liver Cirr...

引用

International Conference on Telecommunications and signal processing (TSP)

作者： Ahmet Ercan Topcu Ersin Elbasi Yehia Ibrahim Alzoubi College of Engineering and Technology American University of the Middle East Kuwait College of Business Administration American University of the Middle East Kuwait

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Liver cirrhosis poses a significant threat as a highly infectious blood-borne illness, often remaining asymptomatic in its initial stages, thereby complicating early diagnosis and treatment. As the disease advances to its later stages, diagnostic and therapeutic interventions become increasingly daunting. This work endeavors to provide a robust solution by introducing an Artificial Intelligence (AI) system driven by state-of-the-art Machine Learning (ML) algorithms, aiming to aid healthcare professionals in the early detection of liver cirrhosis. Multiple ML algorithms are under development with the primary objective of predicting the likelihood of liver cirrhosis infection. Through this research, seven distinct models have been crafted, leveraging diverse parameters and employing different ML algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Random Forest (RF), Multi-layer Perceptron (MLP), AdaBoost, and Bernoulli Naive Bayes (BernoulliNB). Among these, the RF algorithm emerged as the frontrunner, exhibiting an impressive accuracy rate of approximately 98 percent. Utilizing an open-access liver cirrhosis dataset, this methodology surpasses earlier research endeavors, showcasing a substantial improvement in predictive accuracy. Rigorous comparisons between models underscore their robustness, affirming their reliability and establishing a clear pathway forward for future investigations in this domain.

关键词： Radio frequency Accuracy Machine learning algorithms Liver diseases signal processing algorithms Nearest neighbor methods Prediction algorithms

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Economic Load Dispatch with Integration of Electric Vehicles

Economic Load Dispatch with Integration of Electric Vehicles

引用

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

作者： B.Vinod Kumar Jennie Angela Jose Shirley Aneesa Farhan M A Dep. of EEE National Institute of Technology Tiruchirappalli India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

A transition to electric vehicles (EVs) is favored owing to its multifaceted benefits, such as reduced noise and environmental pollution. However, integrating EVs into the grid system may affect its operation. Economic Load Dispatch (ELD) refers to the provision of power to the consumers at the least cost possible while taking into account the active and reactive power constraints specified for the system. This paper implements the ELD of a system, taking into consideration the operational constraints of the Electric Vehicle Charging Statuin (EVCS) and the power system. The primary aim of the algorithm developed is to minimize the cost associated with the system at various stages. The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques have been used to arrive at feasible solutions for a 5-bus system.

关键词： Reactive power Costs Pollution Noise signal processing algorithms Electric vehicle charging Communications technology

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UAV Position Deployment and Power Optimization Based on User Clustering in IoT Network

UAV Position Deployment and Power Optimization Based on User...

引用

Acoustics, Speech, and signal processing Workshops (ICASSPW), IEEE International Conference on

作者： Xiaoye Shi Xiangcheng Su Shu Cai Zhaowei Zhang Haibo Dai School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Taking advantage of their exceptional adaptability, robust maneuverability and user-friendly operation, UAVs have emerged as promising candidates for serving IoT devices on the ground. Considering the aforementioned scenario, this paper proposes a novel multi-UAV 3D position deployment and power optimization algorithm based on user clustering (PD-POBUC). The primary objective of this algorithm is to maximize the quantity of user coverage with the minimum transmit power. Firstly, users are divided into several user clusters by clustering algorithm, and each UAV is restricted to move within the specified user cluster range. Secondly, the iterative algorithm is employed to determine the optimal positions of UAVs for maximizing system user coverage. Finally, by adjusting the coverage radius, the minimum transmission power of the system can be achieved. The simulation results demonstrate that the PD-POBUC algorithm has increased the user coverage compared to benchmark schemes, while also reducing the average transmission power of the system.

关键词： Three-dimensional displays Simulation signal processing algorithms Clustering algorithms signal processing Autonomous aerial vehicles Internet of Things

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Fully-distributed optimization with Network Exact Consensus-GIANT

Fully-distributed optimization with Network Exact Consensus-...

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

作者： Alessio Maritan Ganesh Sharma Subhrakanti Dey Luca Schenato Department of Information Engineering University of Padova Italy Hamilton Institute Maynooth University Ireland Department of Electrical Engineering Uppsala University Sweden

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We consider a fully-distributed optimization problem involving multiple collaborative agents, where the global objective is to minimize a sum of local cost functions. Agents are part of a communication network and can only exchange information with their neighbors. We introduce a novel optimization algorithm called NEC-GIANT, which improves over both GIANT, a popular federated learning algorithm, and Network-GIANT, our previously proposed fully-distributed counterpart of GIANT. NEC-GIANT extends GIANT to the fully-distributed scenario, removing the need for a central server to orchestrate the agents. Unlike the existing Network-GIANT, which suffers from the inefficiency of standard asymptotic consensus, the novel NEC-GIANT is based on finite-time distributed consensus and retains all the convergence properties of the original GIANT. Numerical simulations prove the efficiency and superiority of the proposed algorithm in terms of both iterations and machine run-time.

关键词： Wireless communication Degradation Federated learning signal processing algorithms signal processing Numerical simulation Cost function Servers Standards Convergence

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A Fuzzy Clustering-Based Feature Extraction Approach for Hyperspectral Target Detection

A Fuzzy Clustering-Based Feature Extraction Approach for Hyp...

引用

Workshop on Hyperspectral Image and signal processing: Evolution in Remote Sensing, WHISPERS

作者： Yubo Ma Siyu Cai Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China Chengdu Sichuan China College of Mathematics Sichuan University Chengdu Sichuan China

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

The sufficient spectral information contained in hyperspectral image provides assistance for many applications. But on the other hand, it causes information redundancy and increases computational costs. To surmount these obstacles, an intuitive approach is to reduce dimension. In this paper, a fuzzy relative-weighted K-means (FRWKM) clustering algorithm is proposed to extract feature by integrating target spectral information. First, graph segmentation technique is utilized to determine the optimal number of clusters. Then, the fuzzy cluster centers as well as the membership degree of each band to each center can be obtained based on a weighted fuzzy clustering criterion. Concatenating these centers, the final low-dimensional feature cube is obtained. The target detection experiments on three real hyperspectral images show the preponderance of the proposed FRWKM over the compared dimensionality reduction methods.

关键词： Dimensionality reduction Hands Redundancy signal processing algorithms Clustering algorithms Object detection signal processing Feature extraction Optimization Hyperspectral imaging

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Propagation Distance Estimation for Radio over Fiber with Cascaded Structure

Propagation Distance Estimation for Radio over Fiber with Ca...

引用

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

作者： Dexin Kong Diana Pamela Moya Osorio Erik G. Larsson Dept. of Electrical Engineering (ISY) Linköping University Linköping Sweden

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Recent developments in polymer microwave fiber (PMF) have opened great opportunities for robust, low-cost, and high-speed sub-terahertz (THz) communications. Noticing this great potential, this paper addresses the problem of estimation of the propagation distance of a sub-Thz signal along a radio over fiber structure. Particularly, this paper considers a novel cascaded structure that interconnects multiple radio units (RUs) via fiber for applications in indoor scenarios. Herein, we consider the cascaded effects of distortions introduced by non-linear power amplifiers at the RUs, and the propagation channel over the fiber is based on measurements obtained from transmissions of sub-THz signals on high-density polyethylene fibers. For the estimation of the propagation distance, non-linear least-squares algorithms are proposed, and our simulation results demonstrate that the proposed estimators present a good performance on the propagation distance estimation even in the presence of the cascaded effect of non-linear PAs.

关键词： Optical fiber amplifiers Wireless communication Power measurement Simulation Estimation signal processing algorithms Search problems Optical fiber communication Optical fiber dispersion Terahertz communications

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Bilinear Hybrid Expectation Maximization and Expectation Propagation for Semi-Blind Channel Estimation

Bilinear Hybrid Expectation Maximization and Expectation Pro...

引用

European signal processing Conference (EUSIPCO)

作者： Zilu Zhao Dirk Slock Communication Systems Department EURECOM France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper discusses channel estimation during uplink transmission in Cell-Free (CF) Massive MIMO (MaMIMO) systems. We model the problem as a semi-blind estimation problem with independent and identically distributed (i.i.d.) Gaussian input. Two hybrid Expectation Maximization (EM) and Expectation Propagation (EP) algorithms are proposed to improve convergence behavior. The first algorithm, EM-EP, adopts a vector-level EP approach by treating the per-user channel coefficients and data sequence as EP variables. To make the algorithm tractable, we use the central limit theorem (CLT) to approximate the interference terms and employ EM to construct a majorizer function for the likelihood of the received data, leading to majorization minimization. To further enhance convergence behavior, we propose a matrix-level loop-free EM-EP algorithm. In this algorithm, we treat the channel coefficients and data sequences corresponding to users using the same pilot as EP variables. This method is an alternating minimization algorithm, ensuring convergence. Our simulations verify the effectiveness of the two proposed algorithms.

关键词： Simulation signal processing algorithms Channel estimation Massive MIMO Interference signal processing Approximation algorithms Minimization Uplink Convergence

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A Driver Fatigue Modeling Method Based on Enhanced EEG-EOG Fusion Network for Human-Machine Co-Driving

A Driver Fatigue Modeling Method Based on Enhanced EEG-EOG F...

引用

signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Yinan Wang Yang Zhao Lizhou Gong Yewei Yu Hanxu Liu School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Global R&D Center China FAW Corporation Limited Changchun China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Driver fatigue detection serves as an essential tool in the process of human-machine co-driving, capable of promptly alerting intelligent systems to assume control of the vehicle. The detection of driver fatigue based on EEG signals currently stands as a primary method for assessing the mental state of individuals. However, at the same time, there are still some problems with EEG signals, such as being prone to noise interference and low cross-subject recognition rate. In addressing the aforementioned issues, this study introduces a method for modeling driver fatigue through the fusion of features extracted from EEG and EOG signals. The proposed model incorporates high-precision EEG equipment and good cross-subject EOG equipment as input. Comprising EEGNet, SANet (Spatial Attention Network), SCONV (and Separable Convolutional Network), the model employs a multi-modal approach to assess driver performance and quantify fatigue. The model effectiveness was verified on the SEED-VIG data set. The test results show that the recognition accuracy of this method reaches 91%, which is better than the currently popular model. At the same time, the cross-subject recognition accuracy test was conducted, and the average accuracy reached 87%, which proved the generalization performance of the model across subjects and verified the effectiveness of the model.

关键词： Electrooculography Accuracy Human-machine systems signal processing algorithms Brain modeling Fatigue Feature extraction Electroencephalography Robustness Vehicles

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Comparing algorithms for Loading Classical Datasets into Quantum Memory

Comparing Algorithms for Loading Classical Datasets into Qua...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Andriy Miranskyy Mushahid Khan Udson Mendes Dept. of Computer Science Toronto Metropolitan University Toronto Canada Dept. of Electrical and Computer Engineering University of British Columbia Vancouver Canada Quantum Technologies Team CMC Microsystems Sherbrooke Canada

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum computers are gaining importance in various applications like quantum machine learning and quantum signal processing. These applications face significant challenges in loading classical datasets into quantum memory. With numerous algorithms available and multiple quality attributes to consider, comparing data loading methods is complex. Our objective is to compare (in a structured manner) various algorithms for loading classical datasets into quantum memory (by converting statevectors to circuits). We evaluate state preparation algorithms based on five key attributes: circuit depth, qubit count, classical runtime, statevector representation (dense or sparse), and circuit alterability. We use the Pareto set as a multi-objective optimization tool to identify algorithms with the best combination of properties. To improve comprehension and speed up comparisons, we also visually compare three metrics (namely, circuit depth, qubit count, and classical runtime). We compare seven algorithms for dense statevector conversion and six for sparse statevector conversion. Our analysis reduces the initial set of algorithms to two dense and two sparse groups, highlighting inherent trade-offs. This comparison methodology offers a structured approach for selecting algorithms based on specific needs. Researchers and practitioners can use it to help select data-loading algorithms for various quantum computing tasks.

关键词： Measurement Machine learning algorithms Runtime Loading Qubit signal processing algorithms Machine learning signal processing Optimization Faces

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