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Lane detection networks based on deep neural networks and temporal information

Alexandria engineering Journal 2024年 98卷 10-18页

作者： Lin, Huei-Yung Chang, Chun-Ke Tran, Van Luan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621 Taiwan School of Engineering Eastern International University Binh Duong Viet Nam

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes. © 2024 The Author(s)

关键词： Network architecture

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Data-driven electrical resistance tomography for robotic large-area tactile sensing

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science China(Information sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

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Enhanced Cross Z-Complementary Set and Its Application in Generalized Spatial Modulation

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 4674-4690页

作者： Huang, Zhen-Ming Pai, Cheng-Yu Liu, Zilong Chen, Chao-Yu Institute of Computer and Communication Engineering National Cheng Kung University Tainan701 Taiwan National Cheng Kung University Department of Engineering Science Tainan701 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan701 Taiwan University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

Generalized spatial modulation (GSM) is a novel multiple-antenna technique offering flexibility among spectral efficiency, energy efficiency, and the cost of RF chains. In this paper, a novel class of sequence sets, called enhanced cross Z-complementary set (E-CZCS), is proposed for efficient and feasible training sequence design in broadband GSM systems. Specifically, an E-CZCS consists of multiple CZCSs possessing front-end and tail-end zero-correlation zones (ZCZs), whereby any two distinct CZCSs have a tail-end ZCZ when a novel type of cross-channel aperiodic correlation sums is considered. The theoretical upper bound on the ZCZ width is first derived, upon which E-CZCSs with maximum ZCZ width and flexible parameters are constructed. For optimal channel estimation over frequency-selective channels, we introduce and evaluate a novel GSM training framework employing the proposed E-CZCSs. Numerical results demonstrate that the proposed E-CZCS-based training can achieve the minimum channel estimation mean square error (MSE) and outperform other classes of sequences. © 2020 IEEE.

关键词： Mean square error

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WIP: Pledging to be an Ethical Computing Professional 54

WIP: Pledging to be an Ethical Computing Professional

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Dasigi, Venu G. Estell, John K. Christensen, Ken Bowling Green State University Department of Computer Science Bowling GreenOH United States Ohio Northern University Electrical and Computer Engineering & Computer Science Department AdaOH United States University of South Florida Department of Computer Science and Engineering TampaFL United States

ISBN: (纸本)9798350351507

This innovative practice WIP paper describes the 'Pledge of the Computing Professional' (the Pledge), its need, its relationship to two major codes of ethics, and its role in inspiring computing students to act ethically and professionally. Professional ethics are the heart and soul of any profession. In developing products and services, professionals are to conduct themselves ethically, focusing on the public good. ACM has developed the Code of Ethics and Professional Conduct, and ACM and IEEE computer Society have together published the Software engineering Code of Ethics and Professional Practice. ACM considers ethics a core topic in curricular recommendations. In 2011, a team of computing professionals developed the Pledge of the Computing Professional. The purpose of the Pledge is to acknowledge the impact of computing and the attendant responsibilities and to nurture a desire to promote the public good. The Pledge has now been adopted by over 75 institutions in the US. This paper clarifies the relationship between the Pledge and the codes of ethics. It is hoped that emphasizing this connection and promoting a culture surrounding the Pledge can be instrumental in producing graduates with a well-rounded sense of ethics and professionalism, who become responsible citizens that can shape the profession's future. The Pledge may be viewed as both an introduction for the students to the profound impact and responsibilities they would have in society and a means to inspire them to reflect on its meaning as computing ethics become the center of public discourse. Future work includes making the Pledge international in scope. © 2024 IEEE.

关键词： Students

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High-Performance Chip Design With Parallel Architecture for Magnetic Field Imaging System

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-13页

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Lin, Chun-Hung Yuan Ze University Department of Computer Science and Engineering Taoyuan320315 Taiwan National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung912301 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan International Games System Company Ltd. New Taipei City248 Taiwan

To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetoresistance (AMR) sensors. The core controller of the proposed system was designed using a pipelined parallelization algorithm and implemented on a high-speed field-programmable gate array (FPGA) in a fully hardware-based architecture. The designed MFI system can achieve parallel acquisition of magnetic field components from the 16-channel AMR sensor array, parallel data processing, parallel data transmission, and parallel computations in a single cycle of only 4 ms. Compared with typical detection systems, the proposed solution not only exhibits superior performance but can also provide dynamic visual representations of sensed information. © 1963-2012 IEEE.

关键词： Parallel architectures

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Adaptive Numerology-based Rate Optimization for enhanced-Vehicle-to-Everything Use Cases in Beyond 5G Networks 17

Adaptive Numerology-based Rate Optimization for enhanced-Veh...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Hasan, Nadine Kumar, Abhinav Kataoka, Kotaro Indian Institute of Technology Hyderabad Department of Electrical Engineering Hyderabad India Indian Institute of Technology Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331531195

The existing 5G new radio (NR) numerology supports certain values of parameters like subcarrier spacing, symbol duration, and guard interval for vehicle-to-everything (V2X) communications. However, with ever evolving use cases for enhanced-V2X in beyond 5G networks, these recommended values are not sufficient to provide the required quality of service (QoS). Motivated by this, we propose an adaptive numerology scheme (ANS) that dynamically selects these parameters to mitigate the effect of Doppler and multi-path. We present exhaustive simulation results demonstrating the efficiency of the proposed scheme for the dynamic nature of V2X communication networks. © 2025 IEEE.

关键词： Vehicle to Everything

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Neurosymbolic Reinforcement Learning and Planning: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 1939-1953页

作者： Acharya, Kamal Raza, Waleed Dourado, Carlos Velasquez, Alvaro Song, Houbing Herbert University of Maryland Department of Information Systems Baltimore County BaltimoreMD21250 United States Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL32114 United States University of Colorado Department of Computer Science BoulderCO80309 United States

The area of neurosymbolic artificial intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing subfields, such as neurosymbolic deep learning and neurosymbolic reinforcement learning (Neurosymbolic RL). Compared with traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement learning (RL), a long-standing artificial intelligence (AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this article is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies: learning for reasoning, reasoning for learning, and learning-reasoning. These categories are further divided into subcategories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. In addition, we identify research opportunities and challenges in various applications within this dynamic field. © 2023 IEEE.

关键词： Heuristic algorithms

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Millimeter-Wave GaAs Ultra-Wideband Medium Power Amplifier and Broadband High-Power Power Amplifier for 5G/6G Applications

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IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2024年第1期14卷 111-121页

作者： Fu, Zi-Hao Li, Ming-Xuan Ma, Tzyh-Ghuang Wu, Chan-Shin Lin, Kun-You National Taiwan University Graduate Institute of Communication Engineering Department of Electrical Engineering Taipei10617 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan ASUSTek Computer Inc. Taipei11259 Taiwan Ultraband Technologies Inc. Taipei11158 Taiwan

This paper presents an ultra-wideband (UWB) medium power amplifier (MPA) and a broadband high-power power amplifier (HPA) operating at the 5G/6G frequency bands. By using 0.15~\mu \text{m} GaAs pseudomorphic high electron mobility transistor (pHEMT) technology process, the proposed UWB MPA delivers an average small-signal gain of 16.5 dB, a saturation output power (\text{P}_{\mathrm {sat}} ) of 24 dBm, and a peak power-added efficiency (PAE) over 24% from 24 to 38 GHz with a chip area of 2\times1 mm2. The broadband HPA demonstrates a 17-dB average small-signal gain, 29-dBm \text{P}_{\mathrm {sat}} , and a PAE over 28% from 24 to 32 GHz with a 2.4\times1.1 mm2 chip size. The measurement results have demonstrated the great potential of the proposed PA for 5G/6G millimeter-wave applications. © 2011 IEEE.

关键词： Power amplifiers

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Change Detection in Heterogeneous Remote Sensing Images Based on Copula-Supervised Neural Networks 2

Change Detection in Heterogeneous Remote Sensing Images Base...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Li, Weiming Wang, Xueqian Li, Gang Geng, Baocheng Varshney, Pramod K. Tsinghua University Department of Electronic Engineering Beijing100084 China The University of Alabama at Birmingham Department of Computer Science Birmingham United States Syracuse University Department of Electrical Engineering and Computer Science Syracuse United States

ISBN: (纸本)9798331515669

Change detection (CD) in heterogeneous remote sensing images has been extensively applied in response to emergencies. There have been substantial improvements during the past decades in addressing heterogeneous CD challenges, driven by the rapid advancements in deep neural networks (DNNs). However, most DNN-based heterogeneous CD methods often exhibit black-box behavior, limiting their interpretability and controllability in many practical CD applications. To overcome these issues, we propose a neural copula change detection (NCCD) framework, an approach for the heterogeneous CD problem that leverages an interpretable neural network supervised by copula theory. In NCCD, copula's mathematical characteristics are incorporated as loss functions to supervise a neural network in modeling dependence between bi-temporal heterogeneous remote sensing images. The trained neural network works as a copula function to estimate the degrees of dependence that serve as the indicator for identifying changes. Experimental results on two real heterogeneous CD datasets substantiate the effectiveness of our NCCD. © 2024 IEEE.

关键词： Change detection

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Deep Convolutional Multi-Relational Graph Attention Network for Autonomous Drone Navigation in Urban Wind Zones

Deep Convolutional Multi-Relational Graph Attention Network ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pabitha, C. Joshua, K. Paul Pradeep, S. Sophia, S. Sharma, R. Rajesh Prabhu, D. Karthik Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India MAlla Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Secunderabad India Karunya Institute of Technology and Sciences Department of computer Science and Engineering Tamil Nadu Coimbatore India Alliance University Department of Computer Technology Alliance College of Engineering and Design Bangalore India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalaiyam India

ISBN: (纸本)9798331509828

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41. © 2025 IEEE.

关键词： Mesh generation

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