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A Simple Chaos-Based Compressed Sensing with Encryption Scheme for Wearable IoT Devices 21

A Simple Chaos-Based Compressed Sensing with Encryption Sche...

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21st international conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Wannaboon, Chatchai Ketthong, Patinya Thai Nichi Inst Technol Intelligent Elect Syst Lab IES Bangkok Thailand

ISBN: (纸本)9798350381566;9798350381559

As an advancement of Internet-of-Thing (IoT) technology, wearable devices have become increasingly prevalent. However, ensuring the safety of the data and managing power consumption are crucial issue. This paper presents an integration of compressed sensing and encryption scheme for wearable IoT devices. The advantages of compressed sensing (CS) and chaotic system are integrated, i.e., reducing data transmission and storage requirements and concurrently enhancing the security and randomness of the encryption process. A 1-dimensional signum-based piecewise-linear chaotic map is utilized as a source of random sensing and robust key generation for the encryption. While the sparse measurement matrix effectively reduces the dimensionality of the data. The security and compressive capability are demonstrated through FitBit Fitness Tracker dataset, in terms of compression ratio, reconstruction quality, statistical analysis and security analysis. The results show that the scheme can achieve high compression and encryption efficiency, while facilitating transmission bandwidth without compromising quality of the information. The proposed system ensures minimal computational overhead and enable energy-saving feature that can be easily implemented to various types of wearable technologies.

关键词： Compressed sensing chaotic system chaosbased compression CS and encryption wearable IoT devices

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Predicting Calamansi Fruit Yield Using CNN-Based Flower Detection: A Deep learning Approach 21

Predicting Calamansi Fruit Yield Using CNN-Based Flower Dete...

引用

21st international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Ponce-Machete, Regine A. Fajardo, Arnel Technol Inst Philippines Grad Sch Manila Philippines Isabela State Univ Coll Comp Sci Cauayan Campus Isabela Philippines

ISBN: (纸本)9798350381771;9798350381764

As agriculture integrates advanced technologies to enhance productivity, precise fruit yield estimation emerged as a need. Calamansi, a citrus fruit in Southeast Asia, demands accurate yield projections for effective resource allocation and market responsiveness. Traditional methods lack efficiency, driving the exploration of innovative techniques like Convolutional Neural Networks (CNNs), and You Only Look Once (YOLO). This paper introduces a deep learning approach for calamansi fruit and flower detection, supported by a customized dataset. YOLOv8m model was used for calamansi and flower detection, achieving an F1 score of 0.815, a Recall of 0.794, a Precision of 0.839, and mAP@0.5 of 0.875. The study concluded that YOLOv8m algorithm can significantly enhance the production of fruits in calamansi farms.

关键词： CNN F1 score Precision Recall mAP YOLO

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3D Spatial Information Restoration Based on G-ICP Approach with LiDAR and Camera Mounted on an Autonomous Surface Vehicle 21

3D Spatial Information Restoration Based on G-ICP Approach w...

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21st international conference on Ubiquitous Robots (UR)

作者： Heo, Suhyeon Kang, Minju Choi, Jinwoo Park, Jeonghong KRISO Adv Intelligent Ship Res Div Daejeon South Korea

ISBN: (纸本)9798350361087;9798350361070

In this study, we proposed a 3D spatial information restoration approach using LiDAR and camera to improve the autonomy level of autonomous surface vehicles (ASVs). The preprocessing phase was designed for removal of inherence noise corresponding to data obtained from LiDAR and camera. Because RGB color information is sensitive to changes in illumination, a gamma correction and dark channel prior (DCP) approach was applied to minimize the rate of change of color information due to environmental factors. In addition, because using the LiDAR point cloud data (PCD) source as is would take a long time to process the data, and noise would reduce the accuracy of the data processing, we went through a preprocessing process to remove noise and outliers through filters. Then, the relative coordinate information between the LiDAR and camera was used to calibrate each data in advance, so that the RGB color information was projected on the filtered PCD. Subsequently, accumulated using generalized iterative closest point (G-ICP) approach in order to generate 3D spatial information. The field data obtained in an inland water environment was used to demonstrate the validity of the proposed approach, its results were described.

关键词： Cameras

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High Performance Deep learning GPR Feature Detector Model for Potash Mining 21

High Performance Deep Learning GPR Feature Detector Model fo...

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21st international conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Sadeghikhah, Kaveh Paranjape, Raman Okonkwo, Victor Univ Regina Engn & Appl Sci Regina SK Canada Nutrien Engn Technol & Capital Saskatoon SK Canada

ISBN: (纸本)9798350381566;9798350381559

Ground Penetrating Radar (GPR) has been an essential nondestructive geophysical tool in Saskatchewan's potash mines for over six decades. This innovative technology, used in conjunction with an active boring machine, facilitates real-time data collection, particularly for imaging the immediate clay seam (414-clay seam) above the mine roof. Its reliability is demonstrated by the accuracy with which the roof beam thickness (mine roof to 414-clay seam) is interpreted in real-time, crucial for making informed safety decisions during mining operations. The imperative for a robust auto-picking algorithm tailored to handle complexities in potash mine GPR data is emphasized. The previously developed algorithm called the Clustered Ratio Derivative (CRD) algorithm, employing unsupervised machine learning for real-time GPR interpretation showcased promising results. However, the CRD algorithm faces limitations due to potential sensitivity to variations in input data, especially in the presence of noise or anomalies. Geological variations, such as the presence of "stray clays" within the roof beam, pose challenges to the algorithm's performance and accuracy. In response to these challenges, this paper proposes a novel deep learning-based algorithm leveraging two distinct Convolutional Neural Network (CNN) architectures. These CNNs are designed to navigate the intricacies of the GPR data pattern specific to potash mines. The presented results indicate promising levels of accuracy, with the new method achieving 95.4% accuracy in detecting the 414-clay seam and an average of 88% accuracy in finding "stray clays" seen in the dataset when compared to a geophysicist interpretation. The overall analysis suggests that this new approach has the potential to detect mining room roof features with a high degree of accuracy.

关键词： Ground Penetrating Radar (GPR) Potash Mine Clustered Derivative Ratio (CRD) Deep learning gprMax Convolutional Neural Network (CNN) Machine learning Auto-picking

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Machine learning for Sensor-Based Handwritten Character Recognition: A Brief Survey 21st

Machine Learning for Sensor-Based Handwritten Character Reco...

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21st international conference on Distributed Computing and intelligent Technology

作者： Singh, Shashank Kumar Chaturvedi, Amrita IIT BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9783031814037;9783031814044

Handwriting recognition technologies have evolved significantly, with recent advancements incorporating wearable devices such as smartpens, smartwatches, and smartphones to capture motion and gesture data. This paper reviews various efficient handwriting recognition models that utilize inertial sensors, visual sensors, EMG sensors, and acoustic and Wi-Fi signals. Inertial sensor-based approaches often use accelerometers and gyroscopes to collect three-dimensional data during handwriting, with models achieving high accuracy in recognizing digits and alphabets. Visual sensor-based techniques, leveraging cameras and image processing, have been fundamental in developing Optical Character Recognition (OCR), further enhanced by deep learning algorithms. EMG sensors have been explored for their potential to capture muscle activity for gesture recognition. At the same time, acoustic and WiFi signal-based methods offer innovative ways to recognize handwriting through sound and signal variation. Despite the progress, challenges such as sensor drift, data generalization, and computational requirements persist. Integrating multi-modal sensors and advanced machine learning techniques holds promise for overcoming these limitations and improving the accuracy and usability of handwriting recognition systems across diverse applications.

关键词： Inertial Sensor Machine learning Visual Sensors

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intelligent Flood Prediction and Support: Empowering Communication with AI, IOT, and UAV Technology 21

Intelligent Flood Prediction and Support: Empowering Communi...

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21st international Multi-conference on Systems, Signals and Devices, SSD 2024

作者： Sharma, Ashish Alaubaidy, Mateen Naser College of Engineering and Computer Science Lebanese French University Department of Information Technology Kurdistan Erbil Iraq

ISBN: (纸本)9798350374131

The increasing number of natural disasters in recent years has demonstrated the importance of establishing effective wireless emergency communications services. Unmanned aerial vehicles (U A V s) have become a competitive alternative due to their remarkable capabilities and adaptability. This paper presents the mathematical concept of a UAV-based post-disaster wireless emergency communication network. Our main goal is to use a deep learning algorithm to optimize the UAV's trajectory so that it serves as many ground users as possible during natural disaster. The proposed method not only solves the optimization problem but also maximizes the throughput that can be achieved while respecting the energy constraints. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Proceedings - 21st international Joint conference on Computer Science and Software engineering, JCSSE 2024

Proceedings - 21st International Joint Conference on Compute...

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21st international Joint conference on Computer Science and Software engineering, JCSSE 2024

ISBN: (纸本)9798350381764

The proceedings contain 109 papers. The topics discussed include: a 3D camera protocol for object pose estimation from point cloud in robot operations;diesel price prediction models with traditional time-series algorithms and neural networks;behavioral authentication using activity-based model segregation in convolutional neural networks for mobile sensor data;unveiling emotions and themes in Thai songs via topic modeling;the use of clinical language models pretrained on institutional EHR data for downstream tasks;backward inverted lists approximate string matching algorithm;comparative analysis of pre-trained deep neural networks for plant disease classification;and an investigation of machine learning algorithms for predictive maintenance in high pressure processing systems.

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Analysis and Treatment of Missing data in Happiness Score Using Machine learning 21

Analysis and Treatment of Missing Data in Happiness Score Us...

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21st IEEE international conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Touati, Wassim El Touati, Yamen Hadj-Alouane, Nejib Ben Jemai, Abderrazek Polytechnic School of Tunisia Carthage University SERCOM Laboratory Tunis Tunisia Northern Border University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia ENIT University of Tunis EI Manar OASIS Laboratory Tunisia American University in Dubai Electrical Engineering and Computer Engineering Department Dubai United Arab Emirates

ISBN: (纸本)9798350378078

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible. © 2024 IEEE.

关键词： Adversarial machine learning

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A study on Peg-in-Hole Insertion based on Misalignment Error Estimation Network 21

A Study on Peg-in-Hole Insertion based on Misalignment Error...

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21st international conference on Ubiquitous Robots (UR)

作者： Cho, Taeyeop Kim, Jinseok Choi, Iksu Pyo, Dongbum Univ Hanyang Dept Intelligent Robot Seoul South Korea Korea Inst Ind Technol Ansan South Korea

ISBN: (纸本)9798350361087;9798350361070

Ongoing research is being conducted on the autonomous operation of robots for manufacturing automation. However, it faces challenges due to uncertainty in unstructured environments. Particularly, tasks with frequent contact, such as peg-in-hole assembly, pose a risk of hazardous forces on the robot system due to uncertainty-induced collisions. To address this, we propose a regression learning model to infer the orientation error angles of the peg and hole based on contact data. We demonstrate its effectiveness in overcoming jamming caused by misalignment in peg-in-hole insertion by integrating it with a traditional parallel force/position controller and a learned misalignment error estimation network (MEN). Experimental results show that the system integrated with MEN achieves a 100% insertion success rate and a faster average duration compared to the single system, demonstrating stable peg-in-hole task without jamming.

关键词： Errors

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Policy GRU-RL: Simplified Music Playlist Recommendation Using Sequential on Reinforcement learning Concept 21

Policy GRU-RL: Simplified Music Playlist Recommendation Usin...

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21st international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Chanarong, Chanapa Maneeroj, Saranya Chulalongkorn Univ Fac Sci Dept Math & Comp Sci Bangkok Thailand

ISBN: (纸本)9798350381771;9798350381764

In the realm of streaming services, recommendation systems play a crucial role in meeting user preferences by aiding them in discovering music tailored to their tastes. Reinforcement learning (RL) stands out as a popular method for music recommendations. Nevertheless, prior approaches have grappled with the challenge of over-fitting. After a certain learning period, the agent may struggle to predict actions solely based on past interactions, posing issues for the current user context. To address this limitation, previous methods must be retrained by resetting all parameters in the agent. This study introduces the Policy GRU-RL method, which combines sequential-based learning and reinforcement learning to tackle over-fitting without the necessity of resetting all parameters. This method capitalizes on the features of a recurrent network by implementing an epsilon-greedy policy within the GRU gate. An update gate in the GRU determines whether to choose the random action (current input of the GRU cell) or the optimal action (information from the preceding GRU cell, containing actions with maximum rewards). Additionally, it carries " and action values through each iteration, assessing over-fitting by checking for duplicated predicted actions in specific i terations. Subsequently, the " parameter in the agent is reset. The results demonstrate that our proposed Policy GRU-RL surpasses baseline approaches in terms of accuracy.

关键词： Reinforcement learning

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