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检索条件"任意字段=International Conference on Electrical Engineering Computing Science and Automatic Control"

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automatic Detection of Peripheral Facial Landmarks Using 3D Facial Data

Automatic Detection of Peripheral Facial Landmarks Using 3D ...

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international conference on Image and Vision computing New Zealand, IVCNZ

作者： Mehraan Chowdhury Mohammed Bennamoun Farid Boussaid Syed Mohammed Shamsul Islam Department of Computer Science and Software Engineering University of Western Australia Perth Australia Department of Electrical Electronic and Computer Engineering University of Western Australia Perth Australia Discipline of Computing and Security Edith Cowan University Perth Australia

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Facial landmarking research has traditionally fo-cused on midface landmarks, largely overlooking peripheral facial features that are crucial in specialized medical applications. This study addresses this gap by developing a novel model to detect 12 peripheral landmarks, which are valuable in clinical scenarios such as obstructive sleep apnea detection and facial palsy assessment. Using the FRGC v2 dataset of 3D facial scans, we manually annotated a subset of 900 scans with peripheral landmarks following a rigorous pre-processing protocol. Building upon a high-performance deep learning model initially designed for midface landmark detection, we restructured its architecture to predict peripheral landmarks by modifying its output layers and training it from scratch using 2D projections of 3D scans. We conducted a comprehensive set of experiments, systematically testing different input configurations by varying both the number of images per scan (16, 32, and 64) and the image types (mesh-only, RGB, and depth maps). The model demonstrated its potential by achieving a mean error of 10.72 mm, highlighting the promise of deep learning techniques in addressing the complex challenge of peripheral landmark detection. These findings underscore the model's capability to support niche medical applications and open pathways for future advancements that could refine its accuracy, ultimately contributing to improved diagnostic tools in healthcare.

关键词： Training Deep learning Solid modeling Biomedical equipment Three-dimensional displays Protocols Medical services Transforms Sleep apnea Testing

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Smart Home Security System Using IoT

Smart Home Security System Using IoT

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international conference on computing and Networking Technology (ICCNT)

作者： Peddarapu Ramakrishna K. Sachin Irfan Ahmad Rather M. Vandana S Sai Vignesh Department of CSE VNRVJIET Hyderabad India Computer Science Engineering VNR VJIET Hyderabad India

We are regularly dealing with security vulnerabilities on all fronts. So, we must use streamlined technology to address these problems. In this project, we'll be taking pictures of people and fingerprint impressions to compare with pictures and impressions from the database that have already been stored. Finding out who enters or leaves the property is the most crucial feature of any home security system. As a person's biometric traits, their distinctive faces and fingerprint impressions may be used to monitor that instead of using passwords or pins. We want to create a smart door that protects the entranceway based on who we are. By integrating facial detection and fingerprint impression identification, this project aims to assist users in enhancing the door security of critical locations. The essential components of the proposed system are face detection, face recognition, fingerprint sensor, and automatic door access control.

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Lightweight automatic Modulation Classification Method Using Filter Pruning via Geometric Median

Lightweight Automatic Modulation Classification Method Using...

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Robotics, Intelligent control and Artificial Intelligence (RICAI), international conference on

作者： Chunying Shi Wanyu Zhou Xiangyong Wu Xixi Zhang Yu Wang Guan Gui College of Telecommunications and Information Engineering NJUPT Nanjing China The 23rd Research Institute China Aerospace Science and Industry Corporation Second Academy Beijing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

automatic Modulation Classification (AMC) is a technique employed to determine the modulation type of signals in non-cooperative communication systems. It is crucial in military and civilian communications, offering significant potential for various applications. Recently, AMC methods based on deep learning (DL) are known for their outstanding performance. However, the high computational cost and large model size of DL-based AMC methods limit their application in edge Internet of Things (IoT) systems. Hence, this paper proposes a lightweight AMC method based on filter pruning via geometric median (FPGM). Specifically, we start by determining the geometric median of the filters in each layer, which effectively represents the filter distribution within that layer. Then, filters near the geometric median are identified and filtered out through the characteristics of the geometric median. Finally, the performance degradation of the model caused by the removal of filters can be restored through fine-tuning. Our experimental findings suggest that the proposed FPGM-based AMC method reduces the model size by 99%. At the same time, the classification accuracy is degraded by approximately 1.61%, which significantly outperforms other lightweight AMC methods.

关键词： Deep learning Accuracy Quantization (signal) Military computing Computational modeling Modulation Information filters Complexity theory Internet of Things Robots

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A Novel Collision Detection Protocol for Wireless Full-duplex Networks

A Novel Collision Detection Protocol for Wireless Full-duple...

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IEEE international conference on Networking, Sensing and control

作者： Qinglin Zhao Li Feng MengChu Zhou Haisheng Yu Yi Sun School of Computer Science and Engineering Macau University of Science and Technology Macau China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA International Institute of Next Generation Internet Macau University of Science and Technology Macau China Institute of Computing Technology Chinese Academy of Sciences Beijing China

Conventional wireless networks are half-duplex and most of them use contention-based protocols. These protocols usually adopt a principle of contention with collision avoidance and infer a collision occurrence very late from the absence of an acknowledgement after data transmission, thus causing low network performance. Wireless full-duplex enables simultaneous transmission (TX) and reception (RX) on the same channel. By exploiting this functionality, this work proposes a novel design that enables contention with collision detection (CCD) to improve the network performance. With this design, in contention, a node exploits its TX antenna to transmit a signal for channel contention, while exploiting its RX antenna to sense if other nodes are transmitting too. By checking the status of TX and RX antennas, the node can detect the contention collision before data transmission and hence obtains an opportunity to avoid data collision effectively. This work then develops a theoretical model to analyze the collision probability of this design. Extensive simulation results verify the effectiveness of our proposed design in improving wireless network performance.

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Hybrid Machine Learning and Deep Learning Models for Mental State Classification Using EEG and ECG Signals

Hybrid Machine Learning and Deep Learning Models for Mental ...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Kumar, Jayasheel Soni, Deepak Kumar Nijhawan, Ginni Nagaraju, Krishna Chythanya Abedi, Tamam Ali Abd Ulabbas Subhashini, P. Kalagatoori Archakam New Horizon College of Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Lovely Professional University Phagwara India Griet Department of Cse Bachupally Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331527495

EEG and ECG signals are electrographic measures of brain and heart activity respectively and can indicate neurological states and mental task states. In this paper, we present an enhanced approach of a set of novel temporal features such as energy, Shannon energy, entropy, and temporal energy with state-of-the-art machine learning classifiers to distinguish relaxing from task oriented cognitive states. We besides explore deep learning methods including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for their capacity to recognise complex characteristics within EEG and ECG patterns. We used a publicly available dataset from *** consisting of 36 (male and female) subjects with 21 channels (20 EEG plus 1 ECG). We found that no measure besides Random Forest outperformed all other traditional methods, with 99.34% accuracy. However, deep learning models continued to improve classification, specifically by extending into fusion of multi-modal signals and extraction of temporal features, suggesting the promise of real time cognitive state monitoring. Our results provide a direction for utilizing machine learning and deep learning jointly to improve mental state classification and task performance. © 2025 IEEE.

关键词： Convolutional neural networks

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6G Mobile Communications Systems: Requirements, Specifications, Challenges, Applications, and Technologies 1

6G Mobile Communications Systems: Requirements, Specificatio...

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1st IEEE international Maghreb Meeting of the conference on sciences and Techniques of automatic control and Computer engineering, MI-STA 2021

作者： Yaklaf, Sliman Ka. A. Tarmissi, Khaled S. Shashoa, Nasar Aldian Ambark Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Umm Al-Qura University College of Computers and Information Systems Computer Science Department Makkah Saudi Arabia Libyan Academy for Postgraduate Studies Department of Electrical and Computer Eng. Tripoli Libya

ISBN: (纸本)9781665418560

The Fifth Generation (5G) Wireless networks were planned to be free from the limitations of previous generations. It has become necessary for society to move beyond 5G and design a new architecture that can incorporate the technological needs of both the individual and society. Therefore, if Sixth Generation (6G) mobile communications are to be developed, many aspects must be improved, the most evident being: latency, higher data rate, quality of service (QoS) and system capacity. When looking at the previously mentioned aspiration and requirements of future networks, the need for the development of 6G networks becomes clear. Nevertheless, many challenges which society must overcome for such networks to be implemented exist. The cost of solving such problems pales in comparison to the potential benefits of 6G networks. This paper discusses the challenges of implementing 6G networks. © 2021 IEEE.

关键词： Internet of Everything

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Advanced Spatiotemporal Anomaly Detection in Photovoltaic Power Plants Using Temporal Graph Networks Integrated with Publicly Available Weather Data

Advanced Spatiotemporal Anomaly Detection in Photovoltaic Po...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Reddy, B.Ravali Awasthy, Neeta Keer, Priyameet Kaur Nijhawan, Ginni Mala, Vajenti Albawi, Ali Srinivas, T. Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P. Mathura India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331527495

For purposes of performance analysis and minimization of maintenance expenses, it is vital to identify an effective way in which to monitor anomalies in photovoltaic (PV) power plants. This paper proposes a novel framework to address the problem of anomaly detection based on the inherent graph structure of PV plant configurations using Temporal Graph Networks (TGNs). TGNs are built to capture spatial and temporal dependencies of the PV systems which allows detecting discrepancies affecting the power production, which traditional NN might fail not to notice. The model uses external weather data collected from NOAA and tracks how external parameters influence the performance of photovoltaic panels and therefore electricity generation. Our methodology solely employs TGNs to analyse the intricate relationship in the PV plant data to improve the recognition of abnormality associated with the equipment and weather conditions. The integration of NOAA's open source weather data into the extent of analysis further raises sophistication by offering a variety of features and conditions affecting PV results. Our approach achieves high accuracy of anomaly detection and can be considered to be better compared with the baselines that do not take graph dependencies or time aspect into account. This advancement has important implications for enhancing the operation and maintenance recommendations in the PV power plants and hence the reliability of the satellite power systems. Allying our efforts to one highly specialized method, this research presents a new state of the art in leveraging centralized data for anomaly detection in renewable energy. © 2025 IEEE.

关键词： Network theory (graphs)

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Improving the electrical Machine Efficiency Using Variable Frequency Drive Compared With Multi-Level Inverter by Reduction of Power Loss

Improving the Electrical Machine Efficiency Using Variable F...

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Emerging Trends in engineering and Medical sciences (ICETEMS), international conference on

作者： G. Ramya Lalitha B Sathish Kumar D Rajkumar Chadge Mohammad Rasmi Al-Mousa S. Ravi Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of EEE Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Cyber Security College of Information Technology Zarqa University zarqa jordan University of Business and Technology Jeddah Saudi Arabia Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

The aim of this work is to run and speed control of the machinery using Innovative variable frequency drive for maximum efficiency. A total number of 14 samples are collected from MATLAB simulation, The Proposed Method is an Innovative variable frequency drive and the second Method is Multilevel Inverter where each method consists of 7 samples (GPower was used for sample size calculation). By implementing the Innovative variable frequency drive, the overall efficiency of the machinery is improved and the speed of the machine is controlled easily. Based on the results obtained, it is clear that maximum efficiency is achieved by Innovative variable frequency drive. From SPSS output analysis, the Innovative variable frequency drive has the highest efficiency of (85.85 %) and the Multilevel Inverter has lowest efficiency of (73.80 %). The significance value is 0.55 (statistically insignificant, $\mathrm{p}>0.05$ ). G power is taken as 0.80. It is observed that the simultaneous Innovative variable frequency drive performed significantly better than the existing Multilevel Inverter by reducing the power losses.

关键词： Motor drives Simulation Velocity control Variable speed drives Pulse width modulation Multilevel inverters Market research Power systems Machinery MATLAB

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Hybrid Machine Learning and Deep Learning Models for Mental State Classification Using EEG and ECG Signals

Hybrid Machine Learning and Deep Learning Models for Mental ...

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Intelligent control, computing and Communications (IC3), international conference on

作者： Jayasheel Kumar Deepak Kumar Soni Ginni Nijhawan Krishna Chythanya Nagaraju Tamam Ali Abd Ulabbas Abedi P. Subhashini Kalagatoori Archakam New Horizon College of Engineering Bangalore India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Lovely Professional University Phagwara India Department of CSE GRIET Bachupally Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

关键词： Deep learning Accuracy Electrocardiography Feature extraction Brain modeling Electroencephalography Entropy Random forests Monitoring Long short term memory

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Variable load control and energy efficiency ratio optimization of large refrigeration system based on hybrid optimization algorithm 22

Variable load control and energy efficiency ratio optimizati...

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2022 Asia conference on electrical, Power and Computer engineering, EPCE 2022

作者： Hu, Bin Xu, Ye Xing, Meng Mei, Guanghang Huang, Wenjun College of Control Science and Engineering Zhejiang University Hangzhou China China Petroleum and Chemical Corporation Beijing China

ISBN: (纸本)9781450396127

In order to improve the cooling efficiency of the largescale refrigeration system in the data center under variable load and weather conditions as well as save the operating cost, this paper proposes a fast and accurate hybrid optimization algorithm to optimize the equipment parameters of the refrigeration system. Aiming at lowest energy consumption of the refrigeration system, the particle swarm optimization (PSO) algorithm is used to determine the refrigeration system mode, and the scope of equipment parameters is narrowed through its strong global search characteristic. Then, the Hooke-Jeeves (HJ) algorithm is used to further refine and optimize the equipment parameters, and obtain the final optimized result which means the equipment operating with the lowest energy consumption. Taking the refrigeration system of the data center as the research object, the system model was established based on the equipment mechanism, and the reliability of the simulation model was verified. After that, the hybrid intelligent algorithm was applied to the simulation model to optimize the cooling mode and equipment parameters, and the method in this paper was compared with the traditional optimization control strategy. The results showed that under the weather environment in December, the variable load control strategy based on the hybrid optimization algorithm improved the ratio of the half-power mode and the full-plate heat exchanger (PHE) mode, on the premise of meeting the terminal cooling load demand. The energy consumption of the system was significantly reduced, and the energy consumption ratio of each part of the refrigeration system tended to be reasonable, which would effectively improve the Energy Efficiency Ratio (EER) of the refrigeration system. © 2022 Association for computing Machinery. All rights reserved.

关键词： Energy utilization

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