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Hands-On Gaming: real-time Gesture Detection in Rock-Paper-Scissors Using Mediapipe and CNN

Hands-On Gaming: Real-Time Gesture Detection in Rock-Paper-S...

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2025 international conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Darmawan, Irfan Meldiawati, Aulia Melda Rizal, Randi Rahmatulloh, Alam Haerani, Erna Gunawan, Rohmat Telkom University Department of Information Systems Faculty of Industrial Engineering Bandung Indonesia Siliwangi University Faculty of Engineering Departement of Informatics Tasikmalaya Indonesia Siliwangi University Faculty of Engineering Department of Information Systems Tasikmalaya Indonesia

ISBN: (纸本)9798331513320

real-time hand gesture detection enables intuitive non-verbal communication between humans and devices. In gaming, using hand gestures as a control mechanism enhances user engagement and provides a more immersive experience. This research implements a real-time hand gesture detection system for the Rock-Paper-Scissors game by combining Mediapipe for feature extraction and a Convolutional Neural Network (CNN) for gesture classification. Mediapipe detects and extracts key hand landmarks in real-time, while the CNN classifies the gestures into three categories: rock, paper, and scissors. The proposed system demonstrates high performance, achieving an impressive classification accuracy of 98.12% after 500 training epochs. Furthermore, it ensures adequate detection speed for seamless real-time interaction in gameplay. Future improvements to this system include utilizing a larger and more diverse dataset to enhance the model's ability to recognize a broader range of hand gesture patterns. This approach could improve robustness across different lighting conditions, hand sizes, and variations in gesture execution. Additionally, refining the CNN model architecture could lead to further performance gains in both accuracy and speed. The findings of this research highlight the potential for hand gesture detection to enhance interactivity in gaming and beyond. applications such as augmented reality (AR), virtual reality (VR), and other interactive systems could benefit from this technology. This study demonstrates that integrating Mediapipe and CNNs can provide an accurate, responsive, and engaging user experience, paving the way for further advancements in gesture-based interaction technologies. © 2025 ieee.

关键词： Convolutional neural networks

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Applicability of Deep Learning Model Trainings on embedded GPU Devices: An Empirical Study

Applicability of Deep Learning Model Trainings on Embedded G...

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12th Mediterranean conference on embedded computing, MECO 2023

作者： Chou, Po-Hsuan Wang, Chao Mei, Chih-Shuo National Taiwan Normal University Dept. of Computer Science and Information Engineering Taipei City Taiwan

ISBN: (纸本)9798350322910

The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU. © 2023 ieee.

关键词： embedded systems

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MATE: Masked Autoencoders are Online 3D Test-time Learners

MATE: Masked Autoencoders are Online 3D Test-Time Learners

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ieee/CVF international conference on Computer Vision (ICCV)

作者： Mirza, M. Jehanzeb Shin, Inkyu Lin, Wei Schriebl, Andreas Sun, Kunyang Choe, Jaesung Kozinski, Mateusz Possegger, Horst Kweon, In So Yoon, Kuk-Jin Bischof, Horst Graz Univ Technol Inst Comp Graph & Vision Graz Austria Christian Doppler Lab Embedded Machine Learning Vienna Austria Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Southeast Univ Nanjing Peoples R China

ISBN: (纸本)9798350307184

Our MATE is the first Test-time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MATE also leverages test data for adaptation. Its test- time objective is that of a Masked Autoencoder: a large portion of each test point cloud is removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. We show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, making it extremely lightweight. Our experiments show that MATE also achieves competitive performance by adapting sparsely on the test data, which further reduces its computational overhead, making it ideal for real-time applications.

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ASAP: Reconciling Asynchronous real-time Operations and Proofs of Execution in Simple embedded systems 22

ASAP: Reconciling Asynchronous Real-Time Operations and Proo...

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59th ACM/ieee Design Automation conference (DAC) - From Chips to systems - Learn Today, Create Tomorrow

作者： Caulfield, Adam Rattanavipanon, Norrathep Nunes, Ivan De Oliveira Rochester Inst Technol Rochester NY 14623 USA Prince Songkla Univ Hat Yai Thailand

ISBN: (纸本)9781450391429

embedded devices are increasingly ubiquitous and their importance is hard to overestimate. While they often support safety-critical functions (e.g., in medical devices and sensor-alarm combinations), they are usually implemented under strict cost/energy budgets, using low-end microcontroller units (MCUs) that lack sophisticated security mechanisms. Motivated by this issue, recent work developed architectures capable of generating Proofs of Execution (PoX) for the correct/expected software in potentially compromised low-end MCUs. In practice, this capability can be leveraged to provide "integrity from birth" to sensor data, by binding the sensed results/outputs to an unforgeable cryptographic proof of execution of the expected sensing process. Despite this significant progress, current PoX schemes for low-end MCUs ignore the real-time needs of many applications. In particular, security of current PoX schemes precludes any interrupts during the execution being proved. We argue that lack of asynchronous capabilities (i.e., interrupts within PoX) can obscure PoX usefulness, as several applications require processing real-time and asynchronous events. To bridge this gap, we propose, implement, and evaluate an Architecture for Secure Asynchronous Processing in PoX (ASAP). ASAP is secure under full software compromise, enables asynchronous PoX, and incurs less hardware overhead than prior work.

关键词： embedded systems

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Application and Performance Analysis of Physics-Informed Neural Networks in Solving Complex Physical systems 2

Application and Performance Analysis of Physics-Informed Neu...

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2nd ieee international conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Chen, Xuyang University of California Letters and Science DavisCA United States

ISBN: (纸本)9798350368208

This study introduces a quantum-accelerated framework that integrates quantum optimization with physics-informed neural networks (PINNs) to solve PDEs more efficiently. By leveraging the Quantum Approximate Optimization Algorithm (QAOA), the proposed framework enhances the initialization and boundary parameter optimization of PINNs, addressing key challenges such as slow convergence and high computational cost. The hybrid approach combines the data efficiency and physical constraint embedding capabilities of PINNs with the computational advantages of quantum optimization, enabling faster and more accurate solutions for complex PDEs. The framework was validated using the Shuhao Cao PDE dataset, demonstrating significant improvements in training efficiency and accuracy compared to traditional numerical solvers and classical PINNs. Experimental results highlight that quantum optimization effectively accelerates convergence by optimizing initial conditions and boundary constraints, reducing the computational overhead associated with classical training processes. Despite its reliance on emerging quantum hardware and the sensitivity of PINNs to hyperparameter selection, this approach offers a scalable and robust solution for solving high-dimensional PDEs. The findings underscore the potential of hybrid quantum-classical methods in advancing scientific computing, providing a foundation for future research in stochastic PDEs, multi-physics problems, and other real-world applications. © 2024 ieee.

关键词： Stochastic systems

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Adaptive Computation Offloading for Heterogeneous IoT applications: An Experimental Study 20

Adaptive Computation Offloading for Heterogeneous IoT Applic...

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20th international Multi-conference on systems, Signals and Devices, SSD 2023

作者： Aidaros, Othman Al Bouida, Zied Ibnkahla, Mohamed Carleton University Systems and Computer Engineering Department OttawaON Canada

ISBN: (纸本)9798350332568

Internet of Things (IoT) applications are heterogeneous in terms of the deployed hardware, developed protocols, and requirements of each of these applications. The decision whether to process such applications at the sensor nodes or at a higher layer (i.e., Edge, Fog, or Cloud) heavily depends on those heterogeneity features. In this paper, we propose a number of adaptive schemes to address the local processing vs. offloading of IoT applications based on their energy consumption and execution time. To this end, we deploy six IoT applications on real sensor nodes, measure their energy consumption and execution time, and then decide where to process the application based on the measurements. We study the performance of the proposed schemes in terms of energy efficiency and execution time. We also compare their performances to selected non-adaptive schemes. © 2023 ieee.

关键词： Energy efficiency

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The need for neuromorphic computing in industrial robotics

The need for neuromorphic computing in industrial robotics

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2023 international conference on Artificial Intelligence and Power Engineering, AIPE 2023

作者： Bhanja, Tapanta Nusbaum, Jonathan Abuibaid, Khalil Legler, Tatjana Wagner, Achim Ruskowski, Martin University of Kaiserslautern-Landau Machine Tools and Control Systems Department of Mechanical and Process Engineering Kaiserslautern Germany German Research Centre for Artificial Intelligence Innovative Factory Systems Kaiserslautern Germany

ISBN: (纸本)9798350310788

With Moore's law coming to its limits, the rate of increase in compute power available for processing applications is similarly coming to a halt. This implies that the compute intensive tasks, such as robotics, artificial intelligence, and high-performance space computing need innovative ways to cater their ever-increasing compute needs. One innovative way to solve computational bottlenecks is to bring compute and memory together, as opposed to the Von Neumann computational model, with greater degree of parallelism in an event-based, asynchronous computation paradigm. Neuromorphic computing is one such paradigm that draws its inspiration from the brain. Energy and computational efficiency, asynchronous and event-based processing being its salient features, neuromorphic computing is an area worth exploring for compute intensive tasks. In this paper, the authors explore the possibilities and benefits of neuromorphic computing in robotics, and establish possible research directions that could benefit the robotics domain. © 2023 ieee.

关键词： Robotics

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Predicting Fouling Progress in Water Cooled Chillers Using Bidirectional LSTM Neural Networks and Synthetic Data 1

Predicting Fouling Progress in Water Cooled Chillers Using B...

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1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Hasan, Mohd Faizan Thirumalvalavan, S. Raj, S. Solomon Iftekhar Hussain, Beporam Khan, Maaz Allah Kolekar, Aniket B. Integral University Department of Mechanical Engineering U.P Lucknow India Arunai Engineering College Department of Mechanical Engineering TiruvannamalaiPin - 606 603 India Department of Mechanical Engineering Telangana Hyderabad-75 India Bapatla Engineering College Department of Mechanical Engineering Andhra Pradesh Bapatla522102 India Department of Civil Engineering Uiet Uttar Pradesh Lucknow India D y Patil International University Department of Mechanical Engineering Maharashtra Pune India

ISBN: (纸本)9798350367720

The primary challenge in developing a practical predictive model for equipment cleaning is determining the variables the neural network should calculate, focusing on a cost-benefit analysis. Given the impracticality of using real installation data due to the slow development of deposits, synthetic data were generated using a theoretical model in Engineering Equation Solver, considering variations in external wet bulb temperature and chiller parameters. These simulations, improving upon previous studies, trained neural networks to recognize fouling patterns in new data. The final model, suitable for real applications, was created using Python and executed in Google Colab Notebook, leveraging TensorFlow, Keras, NumPy, Pandas, and scikit-learn libraries. A Bidirectional Long Short-Term Memory (LSTM) architecture with the "tanh"activation function and Adam optimization was adopted, balancing accurate modeling and avoiding overfitting. The model was trained with 10,000 data points, using time, wet bulb temperature, condensation temperature, conductance, pump pressure, and tube length as inputs, and Rf as the output. The results highlighted fouling progression and its impact on chiller performance, demonstrating the viability of this approach. Future work includes testing in real installations, adapting the model using transfer learning, and exploring different architectures and additional physical parameters. © 2024 ieee.

关键词： Water cooling systems

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A real-time cyber-physical testbed to assess protection system traffic over 5G networks

A real-time cyber-physical testbed to assess protection syst...

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2022 ieee international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2022

作者： Adrah, Charles M. Katoulaei, Mohammad K. Amare, Tesfaye Palma, David Norwegian University of Science and Technology Ntnu Department of Information Security and Communication Technology Trondheim Norway Ntnu Department of Electric Power Systems Trondheim Norway Sintef Energy As Trondheim Norway

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

The fifth-generation (5G) mobile network promises to offer low latency services. Hence, there is interest in assessing various power distribution grid applications that can be deployed with a 5G infrastructure. This paper presents a smart grid cyber-physical testbed for protection systems. It consists of power system applications deployed on OPAL-RT, a real-time platform, and a 5G communication network modeled in ns-3. The testbed is used to assess the performance of a power system protection application (Permissive Underreaching Transfer Trip (PUTT) protection scheme) deployed over a 5G communication network. The proposed approach enables real-time protection traffic to be analyzed in an emulated 5G network and gives insights into how such a testbed can be used to assess the performance of protection traffic in 5G networks and beyond. © 2022 ieee.

关键词： 5G mobile communication systems

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A Model for Data Processing on Warehouse-Scale Computers

A Model for Data Processing on Warehouse-Scale Computers

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2024 ieee international conference on Big Data, BigData 2024

作者： Exton, Kevin Read, Maria The University of Melbourne School of Computing and Information Systems Melbourne Australia

ISBN: (纸本)9798350362480

Modern data processing workloads often have highly unpredictable end-to-end latency characteristics that are caused by heterogeneity, time-variation, and parallelized processing. The increase in unpredictability is in part attributable to job "straggling", and is symptomatic of a new class of stochastic scheduling challenges that will degrade the performance of current and future applications at scale. While the job scheduling literature for data processing frameworks is rich with ideas;there is little coordination between research groups on methodology and presentation, stunting the ability for designers to survey a collection of results and draw generalizable conclusions about good design patterns. We introduce an abstract system model for data processing on warehouse scale machines that aids in eliminating ambiguity in the job scheduling research by categorizing schedulers based on where they act on the system in the job processing data pathway. Furthermore, we demonstrate that although the scheduling problem is NP-Hard in the general case, it is still possible to derive scheduler design principles using bounds and asymptotics. © 2024 ieee.

关键词： Data warehouses

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