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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Guorui Zhou Xingjian Wang Northeast Forestry University College of Computer And Control Engineering Harbin China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In response to the issues of high computation and large model parameters in current smoking detection algorithms, making them difficult to deploy on edge devices, this paper proposes an improved lightweight YOLOv8 algorithm for real-time detection and accurate recognition of smoking targets. Firstly, the C2f module in the backbone of YOLOv8 is replaced with a lightweight ContextGuided module, which enhances the accuracy of small object detection through the analysis of feature context, and also reduces model complexity. Secondly, a slim-neck structure paradigm is adopted to further optimize the complexity of the neck part without affecting accuracy. Experimental results show that the improved algorithm reduces the number of parameters, computation, and model size by 17.94%, 20.990%, and 15.78 % , respectively, with mAP@0.5 and FPS reaching 94.9% and 98. This ensures real-time and reliable detection.

关键词： Accuracy Target recognition Computational modeling Feature extraction Real-time systems Complexity theory Neck

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Comparative Analysis of Wearable and computer Vision-Based Methods for Human Torso Posture Estimation

Comparative Analysis of Wearable and Computer Vision-Based M...

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Emanuel Muntean Monica Leba Department of System Control and Computer Engineering University of Petrosani Petrosani Romania

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

This paper conducts a comparative analysis of human torso posture estimation methodologies, focusing on an inertial measurement unit (IMU) sensor coupled with an Arduino UNO as a wearable approach, and Kinect V2, utilizing OpenCV for posture analysis. The core objective of this study is to ascertain which method yields greater precision in the estimation of human torso posture. The IMU sensor, characterized by its wearability, provides the distinct advantage of unobtrusiveness and the capability to record motion across diverse environments. Conversely, Kinect V2 leverages computer vision techniques to derive posture estimations from visual data in real-time. Through comprehensive experimentation, this research evaluates the accuracy of both methodologies by juxtaposing their posture estimation outcomes. The findings of this investigation aim to significantly contribute to the enhancement of human posture estimation systems, with wide-ranging implications for health, sports, and ergonomics.

关键词： Torso Visualization Accuracy Measurement units Estimation Inertial navigation Sensor fusion Reliability Wearable devices Sports

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Level Up Lab Projects' Showcase

Level Up Lab Projects' Showcase

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IEEE Games Media Entertainment (GEM)

作者： Leonardo Pieraccioli Daniel Bologna Gabriele Massimiani Ilaria Pasini Martina Plumari Pietro Uras Francesco Strada Control and Computer Engineering Department Politecnico di Torino Torino Italy

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

The demo aims to showcase the progress made by a student team from Politecnico di Torino called Level Up Lab. The team is developing 5 video games, each with unique characteristics. The games are presented in their alpha version during the demo to gather feedback for improvements in future development iterations. The demos also aim to illustrate the teams' activities and mission to academics and industry professionals to expand its network.

关键词： Video games Entertainment industry Games Media

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Opinion Score Distribution Prediction via AI-Based Observers in Media Quality Assessment

Opinion Score Distribution Prediction via AI-Based Observers...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Lohic Fotio Tiotsop Antonio Servetti Enrico Masala Control and Computer Engineering Department Politecnico di Torino Turin Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Training a Deep Neural Network (DNN) to predict an individual’s opinion score regarding the quality of multimedia content is a recent research direction. This type of DNN is called Artificial Intelligence-based Observer (AIO). By generating individual opinion scores, AIOs enable the prediction of the Opinion Score Distribution (OSD) for a given multimedia content. Multimedia image quality assessment literature lacks contributions that thoroughly assess the ability of AIOs to predict the OSD. In this paper a new set of AIOs is trained and shown to predict the OSD more accurately than state-of-the-art methods.

关键词： Training Image quality Accuracy Artificial neural networks Observers Media Quality assessment Information and communication technology Multimedia communication

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METER: a mobile vision transformer architecture for monocular depth estimation

arXiv

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arXiv 2024年

作者： Papa, Lorenzo Russo, Paolo Amerini, Irene the Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth estimation have gained significant interest in recent years, owing to the potential benefits of this methodology in overcoming the limitations of active depth sensing systems. Moreover, due to the low cost and size of monocular cameras, researchers have focused their attention on monocular depth estimation (MDE), which consists in estimating a dense depth map from a single RGB video frame. State of the art MDE models typically rely on vision transformers (ViT) architectures that are highly deep and complex, making them unsuitable for fast inference on devices with hardware constraints. Purposely, in this paper, we address the problem of exploiting ViT in MDE on embedded devices. Those systems are usually characterized by limited memory capabilities and low-power CPU/GPU. We propose METER, a novel lightweight vision transformer architecture capable of achieving state of the art estimations and low latency inference performances on the considered embedded hardwares: NVIDIA Jetson TX1 and NVIDIA Jetson Nano. We provide a solution consisting of three alternative configurations of METER, a novel loss function to balance pixel estimation and reconstruction of image details, and a new data augmentation strategy to improve the overall final predictions. The proposed method outperforms previous lightweight works over the two benchmark datasets: the indoor NYU Depth v2 and the outdoor KITTI. © 2024, CC BY.

关键词： Learning algorithms

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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Enhancing Electric Buggy Dynamics: A Comprehensive Analysis Using Single-Track Modeling

Enhancing Electric Buggy Dynamics: A Comprehensive Analysis ...

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Computing in Natural Sciences, Biomedicine and engineering (COMCONF), International Conference on

作者： Cosmin Rus Monica Leba Departament of System Control and Computer Engineering University of Petrosani Petrosani Romania

ISBN: (数字)9798350353358

ISBN: (纸本)9798350353365

In an era marked by technological advancements and a shift toward sustainable transportation solutions, dynamic modeling of autonomous electric vehicles is gaining significance. This paper explores the application of the singletrack model in the dynamic analysis of a customized electric Buggy, with the aim of enhancing maneuverability and the predictability of the vehicle's behavior under various operational conditions. Focusing on lateral dynamics, crucial for the precise control of vehicle motion, we have investigated the Buggy's response to different steering commands, analyzing how the interplay between steering, propulsion, and braking contributes to maintaining an accurate trajectory and executing effective evasive maneuvers. A framework based on principles of classical mechanics and control theory has been developed and implemented, which has enabled detailed simulation of the Buggy's interactions with its environment. The vehicle's stability, characterized by parameters such as the cornering stiffness of the wheels and the vehicle's inertia around the z-axis, was validated through eigenvalue analysis using Matlab simulations. These were instrumental in allowing us to visualize the dynamics of the Buggy without resorting to costly physical prototypes and time-consuming field testing. The results of our analysis demonstrate that the design and single-track model parameters selected for our Buggy ensure stability and maneuverability for both urban and off-road conditions, confirming that the system is stable at various speeds. The eigenvalue analysis indicates a system that retains equilibrium post-disturbances, with predictable and controllable vehicle behavior. Furthermore, the real-world applicability of the single-track model has been confirmed, ensuring a high level of safety and comfort for users.

关键词： Analytical models Adaptation models Computational modeling Dynamics Predictive models Electric vehicles Mathematical models Stability analysis Eigenvalues and eigenfunctions Vehicle dynamics

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Wind Turbine Tower Abnormal Monitoring Based on Cloud Model

Wind Turbine Tower Abnormal Monitoring Based on Cloud Model

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第43届中国控制会议

作者： Yali Zhao Qiankun Hu Ketao Dong Peng Guo School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Real-time monitoring of wind turbine output can find the problems in time and ensure the economic benefits of wind farms to the greatest *** power main belt formed by the normal operation data in the two-dimensional coordinate system of wind speed and power can accurately reflect the the power generation performance of a wind ***,Mahalanobis distance is used to calculate the deterioration degree of ***,Mahalanobis distance of monitoring data is analyzed and transformed by sliding window,and the unit state is visually presented by cloud model by specifying fuzzy comment set,and the classification information of normal,early warning and alarm are *** results show that the method can react the operating state of unit in a real,objective,quantitative and qualitative way,also can provide reasonable guidance for the follow-up maintenance work.

关键词： supervisory control and data acquisition(SCADA) abnormal monitoring cloud model wind turbine tower

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ViGEO: an Assessment of Vision GNNs in Earth Observation

arXiv

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arXiv 2024年

作者： Colomba, Luca Garza, Paolo Department of Control and Computer Engineering Politecnico di Torino Turin Italy

Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information on several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus, given the recent advances of machine learning, computer vision and the availability of labeled data, researchers demonstrated the feasibility and the precision of land-use monitoring systems and remote sensing image classification through the use of deep neural networks. Such systems may help domain experts and governments in constant environmental monitoring, enabling timely intervention in case of catastrophic events (e.g., forest wildfire in a remote area). Despite the recent advances in the field of computer vision, many works limit their analysis on Convolutional Neural Networks (CNNs) and, more recently, to vision transformers (ViTs). Given the recent successes of Graph Neural Networks (GNNs) on non-graph data, such as time-series and images, we investigate the performances of a recent Vision GNN architecture (ViG) applied to the task of land cover classification. The experimental results show that ViG achieves state-of-the-art performances in multiclass and multilabel classification contexts, surpassing both ViT and ResNet on large-scale benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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Know Your Bugs: A Survey of Issues in Automated Game Testing Literature

Know Your Bugs: A Survey of Issues in Automated Game Testing...

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IEEE Games Media Entertainment (GEM)

作者： Riccardo Coppola Tommaso Fulcini Francesco Strada Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

As the complexity of video games continues to evolve, so does the importance of effective game testing methodologies. To this end, automated game testing has emerged as a pivotal approach to ensure the quality and functionality of modern games. The objective of the present paper is to identify, through a literature review and the application of Open and Axial coding, the most commonly analysed and mentioned issues in automated game testing literature. The results of the study provide a taxonomy of 26 different issues that are assessed in the software engineering literature by automated game testing practice, grouped in five higher-level categories. The elicited taxonomy can serve as an instrument for game testers, researchers and tool developers to evaluate testing approaches and techniques, enable comparability of research results, and design instruments to investigate functional aspects of games in development.

关键词： Surveys Video games Instruments Taxonomy Entertainment industry Games Media

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