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Enhancing Urban Accessibility: Comparative Analysis of intelligent Traffic signal systems in Major Global Cities

Enhancing Urban Accessibility: Comparative Analysis of Intel...

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2024 international conference on Advanced Technologies for Communications, ATC 2024

作者： Hsiao, Min-Kai Chen, Chien-Min School for Creative Studies Quanzhou University of Information Engineering 249 Bodong Road Fengze District Quanzhou China Software Institute Quanzhou University of Information Engineering 249 Bodong Road Fengze District Quanzhou China International School of Finance Fundan University 220 Handan Road Shanghai China

ISBN: (纸本)9798350353983

As urbanization intensifies, cities worldwide are facing more complex traffic management challenges. The advent of intelligent Transportation systems (ITS) and Internet of Things (IoT) technology offers unparalleled opportunities to improve urban traffic light systems' effectiveness and availability. This paper compares smart traffic signal reforms in Tokyo, Copenhagen, Stockholm, San Francisco, and Hamburg, analyzing their effects on urban accessibility. The study examines three primary perspectives. First, it assesses the influence of intelligent traffic systems on the accessibility of special groups, such as disabled and socially disadvantaged populations, by making precise and responsive adjustments to traffic signals. Second, it investigates enhancements in smart signal control within multi-modal transportation systems, focusing on how algorithms and real-time data processing improve coordination and efficiency. Lastly, it evaluates the overall effectiveness of real-time traffic management implementation. © 2024 ieee.

关键词： Traffic signals

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Transformer acoustic fault diagnosis method based on selective kernel Siamese network modeling 4

Transformer acoustic fault diagnosis method based on selecti...

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4th international conference on intelligent Power and systems, ICIPS 2024

作者： Liu, Yihao Chen, Ying Northeastern Electric Power University Jilin China

ISBN: (纸本)9798331532765

Aiming at the problems of low recognition accuracy and lack of diagnostic reliability faced by the existing transformer non-outage fault diagnosis techniques when dealing with small samples and complex fault scenarios, this paper proposes a transformer acoustic fault diagnosis method based on the selective kernel Siamese network model. An adaptive synchronous S-transform denoising method is proposed to optimize the acoustic signal processing through the S-transform with adaptive windowing to generate the acoustic pattern with high time-frequency resolution and no energy divergence. Further, a time-frequency selective kernel enhancement (TFSK) module is designed for dynamic convolution kernel size selection and expansion rate adjustment, which effectively enhances the ability to capture the time-frequency features of the atlas and its adaptability. Finally, the Siamese network is constructed to evaluate the transformer state, and the dynamic metric function is introduced to enhance the sensitivity to the small-sample scale difference, so as to realize the high-precision acoustic diagnosis under the conditions of few samples and information interference. Experimental results show that the method not only maintains high diagnostic accuracy under different data sample sizes and noise interference conditions, but also proves its excellent generalization ability in fault diagnosis of multi-model transformers. © 2024 ieee.

关键词： Acoustic signal processing

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Privacy-Preserving QR Code Schemes for Data Access and Aggregation in Smart Distribution Grid systems 9

Privacy-Preserving QR Code Schemes for Data Access and Aggre...

引用

9th international conference on intelligent Computing and signal processing, ICSP 2024

作者： Zhang, Zhijun Gao, Yingjian Zhu, Weiping Lu, Kai State Grid Electric Power Research Institute Jiangsu Nanjing China State Grid JiangSu Electric Power Co. Ltd Jiangsu Nanjing China

ISBN: (纸本)9798350376548

The smart distribution grid is a type of electrical supply network that has been widely applied in life. Ensuring efficient and secure communication of information within the smart distribution grid has become one of the hot topics of research. This paper presents a QR code-based data access control and aggregation scheme for smart distribution grid. The scheme utilizes QR codes to store user's electricity consumption information and employs Hamming encoding along with information hiding techniques to embed user's identifiers into the QR codes for authentication by local gateways. Experimental results show that the scheme can effectively resist common image attacks. Compared to existing hiding schemes, this scheme demonstrates certain advantages in terms of security, robustness, and embedding capabilities. © 2024 ieee.

关键词： Smart power grids

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Instant Motion Recognition of Lower Limb Using Surface Electromyography signal Based on Convolutional Neural Network

Instant Motion Recognition of Lower Limb Using Surface Elect...

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2024 ieee international conference on robotics and Biomimetics, ROBIO 2024

作者： Li, Xiaohui Yu, Xiaoyue Zhou, Hao Jin, Dingxun Li, Guanglin Wang, Lin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China University of Chinese Academy of Sciences China Shenzhen Lower Limb Rehabilitation Intelligent Aids Engineering Research Center China Shenzhen Technology University China Southern University of Science and Technology China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781665481090

The field of rehabilitation robotics has emerged as a prominent area of research in the medical community in recent years, offering innovative and promising solutions for patients with physical disabilities. The existing lower limb rehabilitation robots have some shortcomings in human-machine compatibility and motion synergy due to the limitation of the real-time human motion intention recognition. In this paper, an instant motion recognition of the lower limb was proposed using surface electromyography (sEMG) for significantly shortening the time delay between the robot motion and the actual human motion. The first 200ms data of the complete sEMG signal for a single motion cycle was employed in the recognition algorithm. The proposed recognition algorithm demonstrated superior accuracy compared to traditional machine learning methods. The accuracy of 9-class recognition was 94.32%, while 12-class recognition achieved that of 89.32%. A data augmentation method based on magnitude warping followed by shifting was employed to enhance recognition accuracy by 11.37% in 9-class recognition and 19.23% in 12-class recognition when comparing with no augmentation. The optimal results for motion recognition were observed when the first 200ms data of the complete sEMG signal for a single motion cycle with a duration of 1.0s were utilized, as evidenced by comparative results. The proposed method offers a novel approach to enhancing human-machine compatibility and comfort in the application of lower limb rehabilitation robots in the future. © 2024 ieee.

关键词： Convolutional neural networks

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Hierarchical Design of Higher-Order Efficient Wallace Tree Approximate Multipliers with Innovative Compressors

Hierarchical Design of Higher-Order Efficient Wallace Tree A...

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2024 ieee international conference on intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Rao, Talla Srinivasa Srinivasu, Ch. Babulu, K. Jawaharlal Nehru Technological University Gurajada Vizianagaram Department of ECE Vizianagaram India Raghu Engineering College Department of ECE Visakhapatnam India

ISBN: (纸本)9798350379525

Multipliers are essential design elements in Digital signal processing (DSP) but their traditional design consumes a lot of power, area, and time. Approximate computing provides an alternative, particularly in multimedia applications where exact precision is not always necessary. Researchers have explored approximate multipliers to reduce resource usage while maintaining reasonable accuracy. However, there's a need to further optimize resource efficiency without compromising accuracy. It is also crucial to explore how to integrate approximate computing techniques with new architectures. This paper introduces a novel technique for designing approximate multipliers using 5:2 and 4:2 compressors. The approach uses exact compressors for the higher-order bits and approximate compressors for the lower order bits to break down partial products. This hybrid approach aims to improve computational efficiency and reduce resource usage in DSP applications. The study also investigates recursive design methods that combine lower-order multipliers to design higher-order multipliers for enhancing energy efficiency and area utilization. A 4x4 multiplier is designed using innovative compressors, and an 8x8 multiplier is constructed using the proposed 4x4 multipliers in a hierarchical manner. The research conducts thorough simulations to test and analyze the proposed approach. The proposed designs show notable improvements compared to the closest existing systems, with a 16.77% reduction in MRED, an 8.56% reduction in NMED, a 12.82% reduction in delay, an 8.46% reduction in area, and a 21.42 % decrease in power consumption. © 2024 ieee.

关键词： Digital signal processing

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Transcending Information Cocoons: Integrating TransR Embeddings with Tensor Decomposition in Recommender systems 9

Transcending Information Cocoons: Integrating TransR Embeddi...

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9th international conference on intelligent Computing and signal processing, ICSP 2024

作者： Hong, Xin School of Mathematical Sciences Fudan University Shanghai200241 China

ISBN: (纸本)9798350376548

This paper addresses the prevalent challenge of information cocoon in recommender systems, introducing a novel methodology that combines TransR knowledge graph embeddings with CP tensor decomposition. This approach aims to strike a balance between personalization and content diversity, overcoming the limitations of traditional recommender systems that often prioritize similarity over variety. By utilizing TransR embeddings, this method enhances the latent factorization process, aiming to provide more diverse and contextually richer recommendations. The core of the methodology involves the application of TransR embeddings. This is done to deepen the understanding of intricate user preferences and the complex relationships between various content types within recommender systems. By doing so, the approach demonstrates considerable potential in broadening the user experience with varied content, moving beyond the typical constraints of personalized content. This expanded understanding of user-content interaction is pivotal in fostering a more comprehensive user experience through diverse digital landscapes. After applying the TransR graph embedding algorithm to gain an in-depth analysis of the knowledge graph, the tensor decomposition method is used to obtain the preferences of each user while retaining a certain degree of variety. In conclusion, the integration of TransR embeddings with CP tensor decomposition presents a viable solution to the issue of information cocoon, encouraging exploration and variety in recommender systems. This finding suggests a meaningful advancement in balancing user preference with content diversity, contributing to the evolution of recommender systems. This approach has the potential to enhance user engagement by providing a more varied and enriching digital experience. © 2024 ieee.

关键词： Tensors

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A Comprehensive Evaluation of Machine Learning Techniques for Predicting Software Bugs 4

A Comprehensive Evaluation of Machine Learning Techniques fo...

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4th international conference on robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Sakib, Fahim Faysal Islam Juel, Md. Sajjadul Tafannum, Zinniya Joy, Jihad Shahriar Karim, Razuan American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331530938

Software Bug Prediction (SBP) is essential to control overall software quality. As systems become complex, faults increase, and early prediction is needed to enhance quality and reduce expenses. This paper provides a technical study of thirteen Machine Learning (ML) techniques for predicting software bugs by utilizing NASA's Promise Repository datasets. The study examines the effectiveness of these classification techniques focusing on evaluation metrics such as Precision, Recall, F-measure, Accuracy, Error Rate, FPR, FNR, ROC, and MCC. The results indicate that Random Forest (RF) achieved the highest accuracy on PC1 (94.96%) and CM1 (96.60%), while Multilayer Perceptron (MLP) scored 85.6% on KC1, and Logistic Regression (LR) got 86.25% on KC2. Regarding error rates, RF had the lowest on PC1 (0.050), LR on KC2 (0.137). For FPR, SVM, MLP, LR, RBF, ANN, and AdaBoost excelled on PC1 (0.00). For FNR, Decision Tree (DT) performed best on PC1 (0.611), KC2 (0.518), and LDA on CM1 (0.500). © 2025 ieee.

关键词： Adversarial machine learning

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Optimizing Handwritten Character Recognition systems: Neural Networks vs. Statistical Methods

Optimizing Handwritten Character Recognition Systems: Neural...

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2024 ieee international conference on intelligent systems and Advanced Applications, ICISAA 2024

作者： Agrawal, Vikash K. Bogireddy, Srinivasa Rao Patil, Lalit N. Sonekar, Mahesh Patil, Yashraj M. Panwar, Vikas Singh Dr. D. Y. Patil Institute of Technology Department of Automation and Robotics Engineering Pune India Horizon Systems Inc PhoenixAZ United States Dr. D. Y. Patil Institute of Technology Department of Mechanical Engineering Pune India

ISBN: (纸本)9798331539948

This investigation focuses on the identification of handwritten characters using neural network technology combined with statistical methods. Handwriting recognition has gained popularity due to its wide range of applications in document processing, signature authentication, and digitized communication. The use of neural networks, particularly deep learning models, has greatly enhanced the accuracy and efficiency of character recognition systems. This study provides a comprehensive analysis aimed at evaluating the effectiveness of neural networks in recognizing handwritten characters. Various neural network architectures, including convolution and recurrent neural networks, are explored;each designed to address specific challenges in character recognition. Additionally, statistical methods are employed for feature extraction, further improving recognition accuracy. Extensive testing and benchmarking are conducted on diverse handwritten datasets to assess the performance of the proposed approaches. The results demonstrate the significant benefits of combining artificial neural networks with statistical techniques to achieve high levels of accuracy in character recognition, paving the way for practical applications. This study makes substantial contributions to the field of handwritten character recognition by highlighting the synergistic advantages of neural networks and statistical methods, which will aid in the development of more accurate and robust authentication systems for future applications. © 2024 ieee.

关键词： Convolution

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Pilot Contamination in Massive MIMO systems: Challenges and Future Prospects 20

Pilot Contamination in Massive MIMO Systems: Challenges and ...

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20th ieee international Wireless Communications and Mobile Computing (ieee IWCMC) - Green and intelligent Communications

作者： Saeed, Muhammad Kamran Khokhar, Ashfaq Ahmed, Shakil Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)9798350361261;9798350361278

Massive multiple input multiple output (M-MIMO) technology plays a pivotal role in fifth-generation (5G) and beyond communication systems, offering a wide range of benefits, from increased spectral efficiency (SE) to enhanced energy efficiency and higher reliability. However, these advantages are contingent upon precise channel state information (CSI) availability at the base station (BS). Ensuring precise CSI is challenging due to the constrained size of the coherence interval and the resulting limitations on pilot sequence length. Therefore, reusing pilot sequences in adjacent cells introduces pilot contamination, hindering SE enhancement. This paper reviews recent advancements and addresses research challenges in mitigating pilot contamination and improving channel estimation, categorizing the existing research into three broader categories: pilot assignment schemes, advanced signal processing methods, and advanced channel estimation techniques. Salient representative pilot mitigation/assignment techniques are analyzed and compared in each category. Lastly, possible future research directions are discussed.

关键词： 5G Massive-MIMO Pilot Contamination B5G Deep Learning Pilot Assignment Channel State Information

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Terrain-Aware Kinodynamic Planning with Efficiently Adaptive State Lattices for Mobile Robot Navigation in Off-Road Environments

Terrain-Aware Kinodynamic Planning with Efficiently Adaptive...

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ieee/RSJ international conference on intelligent Robots and systems (IROS)

作者： Damm, Eric R. Gregory, Jason M. Lancaster, Eli S. Sanchez, Felix A. Sahu, Daniel M. Howard, Thomas M. Univ Rochester Robot & Artificial Intelligence Lab Hajim Sch Engn & Appl Sci Rochester NY 14627 USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665491907

To safely traverse non-flat terrain, robots must account for the influence of terrain shape in their planned motions. Terrain-aware motion planners use an estimate of the vehicle roll and pitch as a function of pose, vehicle suspension, and ground elevation map to weigh the cost of edges in the search space. Encoding such information in a traditional two-dimensional cost map is limiting because it is unable to capture the influence of orientation on the roll and pitch estimates from sloped terrain. The research presented herein addresses this problem by encoding kinodynamic information in the edges of a recombinant motion planning search space based on the Efficiently Adaptive State Lattice (EASL). This approach, which we describe as a Kinodynamic Efficiently Adaptive State Lattice (KEASL), differs from the prior representation in two ways. First, this method uses a novel encoding of velocity and acceleration constraints and vehicle direction at expanded nodes in the motion planning graph. Second, this approach describes additional steps for evaluating the roll, pitch, constraints, and velocities associated with poses along each edge during search in a manner that still enables the graph to remain recombinant. Velocities are computed using an iterative bidirectional method using Eulerian integration that more accurately estimates the duration of edges that are subject to terrain-dependent velocity limits. Real-world experiments on a Clearpath robotics Warthog Unmanned Ground Vehicle were performed in a non-flat, unstructured environment. Results from 2093 planning queries from these experiments showed that KEASL provided a more efficient route than EASL in 83.72% of cases when EASL plans were adjusted to satisfy terrain-dependent velocity constraints. An analysis of relative runtimes and differences between planned routes is additionally presented. These results reinforce the importance of considering kinodynamic constraints for motion planning in non-flat environme

关键词： signal encoding

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