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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

引用

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

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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Human Age Estimation Based on Dental Images 3

Human Age Estimation Based on Dental Images

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Raidzan, Nur Ain Mohtar, Itaza Afiani Mustafa, Wan Azani Almusawi, Muntather Hussein, Abbas Hameed Abdul Computer Science Studies College of Computing Teknologi MARA Perak Branch Informatics and Mathematics Universiti Perak Malaysia Faculty of Electrical Engineering & Technology Campus Pauh Putra Perlis Arau Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Predicting age is one of the most important characteristics in the forensic context for human identification because biological profiles can only be effectively rebuilt by estimating the most precise age. Humans usually count their age using calendars that are pre-programmed to keep track of days. It is not difficult for any living human being to measure their age, but it becomes a challenge when forensics needs to determine the exact age of a dead person involved in a criminal case. By comparing the stage of tooth development in X-rays to established dental growth norms, forensic scientists may estimate age. To deal with such problems, an age estimate system that predicts a person's real age group is required. In this paper, Convolutional Neural Network (CNN) was used to build Human Age Estimation System that can predict a person's actual age group. The system employs Inception-v3 architecture to create the model. The dataset consists of 1404 dental X-ray images of pre-teen in Malaysia. The model was trained using 60% of the dataset, and the remaining 40% was used for testing. Accuracy, precision, recall, and f-measure were used to assess the model. The model achieved 82%, 78%, 76%, and 77%, respectively. As a conclusion, a large amount of data is required for the training and testing process to acquaint the model with the pattern of tooth growth by age group. It is also crucial to rule out any illnesses that impact tooth growth, as well as radiographic images that are not of good quality. © 2023 IEEE.

关键词： age estimation convolutional neural network forensic Inception-V3 machine learning pre-teen dental images)

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Evolutionary Optimization Algorithm Based Optimal Tuning of Cascaded Control Scheme for Single-Stage AC-DC Solid-State-Transformers

Evolutionary Optimization Algorithm Based Optimal Tuning of ...

引用

International Conference on Power Electronics and Motion Control (IPEMC)

作者： Jaydeep Saha Diptak Pal Sanjib Kumar Panda Department of Electrical and Computer Engineering (ECE) College of Design and Engineering (CDE) National University of Singapore (NUS)

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

The cascaded control loops of a grid-connected Solid-state-transformer (SST) are conventionally tuned through small-signal-model (SSM) based root-locus or frequency-response analyses, which often leads to sub-optimal tuning of respective control loops. Evolutionary algorithm based optimal tuning of the cascaded control strategy for a single-stage AC-DC SST is investigated in this paper. Particle-swarm-optimization (PSO) and genetic-algorithm (GA) based optimization techniques are used for offline tuning of the SST’s secondary power balance control (PBC) through iterative minimization of the concerned control loop’s steady-state integral-squared-error (ISE) obtained via detailed co-simulation model. The obtained optimal controller tuning parameters are plugged into the single-stage SST-based interface’s full-scale, 22 kV utility grid-connected, 1 MVA simulation model to demonstrate the performance of the PSO and GA based optimally tuned controller compared to the conventional SSM based counterpart, which elicits the merits of evolutionary algorithm based optimal tuning of cascaded control.

关键词： Heuristic algorithms Dynamics Evolutionary computation Power electronics Topology Steady-state Motion control

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Support Vector Machines Based Predictive Seizure Care using IoT-Wearable EEG Devices for Proactive Intervention in Epilepsy 2

Support Vector Machines Based Predictive Seizure Care using ...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Srinivas, Porandla Arulprakash, M. Vadivel, M. Anusha, N. Rajasekar, G. Srinivasan, C. Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chenagalpattu Tamil Nadu Chennai India Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Vellore Institute of Technology School School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Epilepsy, a neurological illness that causes repeated seizures, can interfere with everyday life and needs prompt treatment. Internet of Things (IoT) wearable Electroencephalogram (EEG) devices and Support Vector Machines (SVM) for predictive analytics are used in this study to suggest a unique strategy for proactive seizure treatment. Wearable EEG devices feed real-time brain activity data. It uses SVM, a strong machine learning method, to build a prediction model using historical EEG data to identify and predict seizures. The prediction software analyses EEG data in real-time to detect pre-seizure patterns and initiate preventive treatments. The seizure prediction method uses SVM's capacity to handle high-dimensional data and catch complicated patterns to improve accuracy and reliability. Healthcare practitioners and caregivers may get timely warnings and react efficiently thanks to the IoT infrastructure's seamless connectivity between wearable devices and a centralized monitoring system. It discusses the ethical and privacy issues of installing such a system, stressing user permission and data protection. Pilot investigations show promising prediction accuracy and reaction time. SVM with IoT-wearable EEG sensors for predictive seizure care offers a forward-looking technique for enhancing epilepsy patients' quality of life by enabling individualized and proactive treatments. © 2024 IEEE.

关键词： Electroencephalography

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Impact of Grid Faults on Grid-Following Converter Controller Operation and Phase Instability

Impact of Grid Faults on Grid-Following Converter Controller...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Inchan Hong Yeuntae Yoo Seungmin Jung Department of Electrical Engineering College of Engineering Myongji University Yongin-si South Korea Department of Electrical and Computer Engineering University of Seoul Seoul South Korea

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

Currently, most Inverter-Based Resources (IBRs) connected to power systems use Grid Following Inverter (GFL) models. However, as the penetration of IBRs in power systems increases, concerns about inverter control issues in weak grids are growing. Particularly, when faults occur in weak grids, IBRs produce voltage with uncontrolled phase and magnitude, leading to their disconnection from the grid. This phenomenon is not observed in traditional generator models, highlighting the need for a detailed analysis of the IBRs controller's operation. This paper analysis the relationship between grid strength and inverter controller operation during grid faults. When fault occurs in a weak gird fault current components inject IBRs controller, consequently IBRs output uncontrolled phase voltage. The voltage with uncontrolled phase causes a three-phase imbalance in the output voltage of the inverter. This phenomenon becomes more severe as the system becomes more unstable. As a result, the output of IBRs is stopped, and they are disconnected from the grid. To verify IBRs controller operation during grid faults, PSCAD/EMTDC is used. To this analysis It will help in establishing criteria for the connection of a weak grid.

关键词： Renewable energy sources Filters Grid following Stability criteria Estimation Power system stability Robustness Voltage control Grid forming Fault currents

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Rural Broadband Access via Clustered Collaborative Communication

Rural Broadband Access via Clustered Collaborative Communica...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Chakravarty, Sumit Banerjee, Madhushri Syed, Tamseel Mahmood Kennesaw State University Department of Electrical and Computer Engineering United States Georgia Gwinnett College Department of Information Technology United States University of Alabama in Huntsville Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331504847

Traditional broadband solutions are often economically and technically infeasible in rural regions due to sparse populations, high infrastructure costs, and energy inefficiencies. This makes rural broadband access a critical challenge, particularly in developing countries where socio-economic disparities and geographical barriers exacerbate the digital divide. This paper proposes an innovative cluster-based network architecture to address such challenges by leveraging low-power Customer Premise Equipment (CPE) and underutilized television white space (TVWS) frequencies. The proposed system forms collaborative clusters of CPEs that use distributed beamforming (DBF) for transmitting data to a central Point of Presence (PoP) Base Station. This approach reduces the need for extensive infrastructure and high-power transmissions, offering a cost-effective and energy-efficient solution. A two-tier uplink access protocol is introduced to optimize data transmission, ensuring scalability and reliability as rural populations grow. By addressing the unique challenges of rural connectivity, this study provides a viable pathway for expanding internet access, enabling socio-economic growth, and bridging the digital divide in underserved communities. Key contributions include dynamic clustering algorithms, scalability enhancements, and a comprehensive performance evaluation, offering a transformative model for rural broadband deployment. Simulation results demonstrate that the proposed architecture is up to 20% more energy efficient with significantly improved throughput compared to conventional rural broadband access schemes. © 2025 IEEE.

关键词： collaborative clustered CPEs distributed beamforming (DBF) underutilized television white space (TVWS)

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Comparative Study of Circular and Rectangular Microstrip Patch antennas in Wi-Fi Band 2

Comparative Study of Circular and Rectangular Microstrip Pat...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Ahmed, Samir Ismail, Taisir Attar, Hani Hafez, Mohamed Solyman, Ahmed Agieb, Ramy Said Mti University Faculty of Engineering Department of Electrical Engineering Cairo Egypt Zarqa University Department of Electrical Engineering Zarqa Jordan Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom

ISBN: (纸本)9798350373363

The growing ubiquity of Wi-Fi has necessitated the development of small and effective antenna designs, especially microstrip patch antennas. In the Wi-Fi frequency range of 2.4 GHz to 2.4835 GHz, this paper compares the performance of circular and rectangular microstrip patch antennas. The study aims to evaluate their performance regarding radiation characteristics, gain, and bandwidth to help choose antennas for Wi-Fi applications. According to the results, the rectangular patch antenna with a rectangular slot outperforms the circular patch antenna with a rectangular slot regarding return loss, coming in at roughly -37.07 dB. Furthermore, the Voltage Standing Wave Ratio (VSWR)value of the rectangular patch antenna is 1.02, which is higher than that of the circular patch antenna, which is 1.34. The rectangular patch antenna has the most excellent radiation efficiency (64.5%) and offers the broadest bandwidth (about 115 MHz). Engineers and researchers looking to enhance antenna designs for Wi-Fi applications can learn much from this study by considering variables like radiation efficiency, bandwidth, VSWR, and return loss. © 2023 IEEE.

关键词： Microstrip antennas

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Long Distance Ad-Hoc MAC Protocol for UAV Networks

Long Distance Ad-Hoc MAC Protocol for UAV Networks

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2025 IEEE SoutheastCon, SoutheastCon 2025

ISBN: (纸本)9798331504847

Unmanned Aerial Vehicle (UAV) networks are increasingly employed in mission-critical applications, demanding efficient and reliable communication protocols to address key challenges such as energy efficiency, collision avoidance, scalability, and Quality of Service (QoS). This paper presents the development of a Long-Distance Ad-Hoc Medium Access Control (MAC) protocol tailored for UAV networks to address such issues. This novel protocol combines hybrid MAC techniques, utilizing Time Division Multiple Access (TDMA) for inter- cluster communication and Code Division Multiple Access (CDMA) for intra-cluster transmissions ensuring resource utility efficiency and high throughput. Key features of the protocol include dynamic power control, proximity-based slot allocation, UAV trajectory sharing, and priority-based scheduling to support real-time data transmissions. Simulation results demonstrate that the protocol has up to 15-20% improved energy efficiency, ensures 100% successful collision resolution, provides scalable solutions for up to 100 nodes, and enhances QoS with a success rate of up to 95% for high-priority traffic. These results highlight the protocol's effectiveness in meeting the demanding requirements of real time communications in UAV networks with potential to support large-scale, energy- efficient, and collision-free communication in dynamic environments. © 2025 IEEE.

关键词： Drones

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Speech Measuring Following Semiconductor Feature for Acoustic using Biodegradable Material 3

Speech Measuring Following Semiconductor Feature for Acousti...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Upadhyay, Shrikant Kumar, Rakesh Kumar, Binod Kumari, Barkha Lakra, Ankita Mandal, Sampurna Department of Electronics and Communication Engineeing MLR Insitute of Technology Hyderabad India Wipro Engineering Edge Hyderabad India Facultyt of CSE & IT Jharkhand Rai University Ranchi India GH Raisoni College of Engineering and Management Pune India Faculty of Computing and IT Usha Martin Univerity Ranchi India Department of Computer Science and Engineering-Artifical Intelligence Brainware University Kolkata India

ISBN: (纸本)9798331527549

This research paper primarily focuses to identify the various feature removal presence used for addresser recognition and validation. The current template i.e. classical and typical are considered to examine its role in different acoustic surroundings and also try to measure feature removal in acoustic condition considering factors like jitters, noise, echo etc. which truly a factor that affect the speech performance and degrades the overall performance of any system. Effort has been put to inspect the attainment of various feature removal approach with the help of classifier considering different structures. The behavior of different feature extraction removal simulated using Hindi samples and it will be analysed in terms of efficiency and error rate. The result may be useful for suitable for existing as well as any proposed model by changing its classifiers. Speech of different ages as well groups are used to analysed and implementation using 16-bit TMS 320 C5X semiconductor devices with the feature extraction and algorithm may improve the overall efficiency of the proposed system. Bio-degradable material can also help to reduce the acoustic level at different of speaker identifications. The feature of semiconductor helps to understand and also add the improvements in proper selection of feature extraction that consume less time in evaluating the algorithm and provide better efficiency in different acoustic conditions. Biodegradable material has not used so far in various studies and biodegradable paly a very important role in acoustic condition as we are dealing with acoustic condition and it is very important to take care of acoustic to come up with better efficiency. © 2025 IEEE.

关键词： Semiconductor devices

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On Microgrid Protection Using Synchronized Phasors

On Microgrid Protection Using Synchronized Phasors

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IEEE Texas Power and Energy Conference (TPEC)

作者： Jorge I. Cisneros-Saldana Miroslav M. Begovic Department of Electrical and Computer Engineering Texas A&M University College Station USA

This paper proposes a reliable technique to leverage current measurements while using phasor measurement units (PMUs). The estimation of Phasors typically relies on the Discrete Fourier Transform (DFT) algorithm, to overcome its drawbacks, the Recursive-Discrete Fourier Transform (R-DFT) algorithm could be used. This study synchronizes off-nominal grid frequen-cies in phase current measurements, a common DFT analysis drawback faced in PMUs implemented in islanded microgrids, amidst voltage-based phase measurements. The proposed method uses all three-phase voltage Phasor measurements to track fun-damental frequency changes and consequently adjust all three-phase current Phasor measurements individually. This approach supports per-phase analysis for its applicability in both balanced and unbalanced conditions in microgrid operation. It offers insights into individual phase behaviors and equips the protection system to detect and mitigate symmetrical and asymmetrical disturbances. The simulation outcomes vividly show the efficacy of our method in reducing errors arising from off-nominal fundamental frequency variations. The results underscore the method's ability to differentiate between short circuit-type faults and typical tapped loads within the protection zone. This innovative approach has the potential to improve the performance of immediate microgrid protection schemes and ensure reliable operation within them. Based on our results, per-phase analysis has shown to be a more versatile and comprehensive protection strategy in scenarios where microgrids face unbalanced and asymmetrical disturbances, as opposed to a more traditional symmetrical component fault analysis because of the special nature of inverter response under abnormal conditions.

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