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IEEE International Conference on Industrial Technology (ICIT)

作者： Ren Fujimoto Chee Siang Leow Hideaki Yajima Koji Makino Xiaoyang Mao Hiromitsu Nishiizaki Dept. of Mechatoronics University of Yamanashi Kofu Japan Dept. of Computer Science and Engineering University of Yamanashi Kofu Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents a novel autonomous navigation system for grapevine cultivation robots operating in overhead trellis vineyards, a challenging environment characterized by irregular grape cluster distribution and complex 3D structures. The system integrates two complementary navigation approaches: high-precision positioning under target grape bunches using YOLOv8-based object detection of QR codes, and large-scale vineyard navigation using global navigation satellite system (GNSS). Each QR code uniquely identifies a grape bunch and provides access to its associated cultivation data. Experimental evaluations in a simulated vineyard environment demonstrated an 83.3% success rate for object detection-based positioning beneath grape bunches and 90% accuracy for GNSS-based navigation within a 1.0 m radius of the target locations. These results validate the system's effectiveness in automating navigation tasks within overhead trellis vineyards, offering a promising solution to address labor shortages and improve the efficiency of grape cultivation practices.

关键词： Precision agriculture Global navigation satellite system Three-dimensional displays Accuracy Service robots Pipelines QR codes Object detection Autonomous robots

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Image Representation of Numerical Data-points for Classification Using Convolutional Neural Network 4

Image Representation of Numerical Data-points for Classifica...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Deka, Rup Kumar Kalita, Kausthav Pratim Chettri, Sarat Kumar Assam Don Bosco University Dept. of Computer Science and Engineering Assam Guwahati-17 India Assam Don Bosco University DDept. of Computer Applications Assam Guwahati-17 India

ISBN: (纸本)9798350323771

Higher dimensional data is a problem for classification. Researchers put forward various approaches like dimension reduction, ranking features for an optimal set, different feature extraction techniques, etc. for classification. In the current context of categorization, CNN (Convolution Neural Network) is a powerful tool, which classifies the data based on images as input. In this work, we have demonstrated a method to represent individual samples in an image, irrespective of dimension, and used CNN for classification. Further, to preserve data privacy, the images are flipped randomly (horizontal and vertical flips) for classification using CNN. The outcomes of both approaches are compared with related techniques and have shown promising results. © 2023 IEEE.

关键词： Classification (of information)

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Automated Traffic Light Control System for Ambulance Using Laser Signals and Raspberry Pi

Automated Traffic Light Control System for Ambulance Using L...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Anusha M S Uttam Upendra Hegde N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Networks & IoT Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Networks & IoT Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Urban traffic congestion poses a major challenge, particularly for emergency services like ambulances, where delays can have life-threatening consequences. This paper proposes a novel, cost-efficient Automated Traffic Light Control System aimed at prioritizing ambulances at busy intersections, thereby reducing delays and ensuring faster response times. The system utilizes Raspberry Pi Zero 2W, Laser Diodes (KY-008), and Light Dependent Resistors (LDRs) to detect ambulances and dynamically control traffic lights. Unlike traditional systems relying on GPS or RFID technologies, this laser-based approach offers greater precision and affordability through the use of modulated laser signals. Experimental results reveal that this system reduces ambulance response times by 35%. The tests were conducted under simulated urban conditions, highlighting the system's capability to outperform conventional solutions. The simplicity of integration and real-time responsiveness underscore its potential for widespread urban adoption.

关键词： Resistors Semiconductor lasers Traffic control Emergency services Road safety Real-time systems Delays Time factors Traffic congestion Radiofrequency identification

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Using BASIN-ML for Machine Learning-Based Statistical Analysis and Reporting for Biofilm Datasets

Using BASIN-ML for Machine Learning-Based Statistical Analys...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Haas, Sam Hartman, Timothy W. Gurung, Bichar Dip Shrestha Do, Tuyen Sani, Rajesh Gadhamshetty, Venkataramana Gnimpieba, Etienne University of South Dakota Dept. of Biomedical Engineering Sioux Falls United States University of South Dakota Dept. of Computer Science Vermillion United States South Dakota School of Mines and Technology Dept. of Chemical and Biological Engineering Rapid City United States Dept. of Civil and Environmental Engineering South Dakota School of Mines and Technology Rapid City United States

ISBN: (纸本)9781665468190

Biological and biomedical microscope image (bioimage) comparison remains useful to approach many research challenges - from biofilms to human diseases. This powerful technology allows researchers to provide the community with a quick visual snapshot of varying experimental conditions. But a two-condition comparison still relies on a researcher's eyes to draw conclusions despite the availability of multiple - often complex - digital image analysis tools. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an easy, objective, reproducible comparison leveraging inferential statistics to bridge image data analysis with other biomedical data modalities such as gene expression. Users have access to a machine learning module to assist with image segmentation using modern, trainable algorithms. BASIN also provides several key data points including images' object counts, net and mean pixel intensities, net and mean object surface areas, plus a variety of other potentially useful data. Hypothesis testing is performed on mean object intensities and surface areas using the statistical power of the R programming language. These features allow BASIN to extend the current scope of image comparison. It gives researchers a multi-model knowledge about matters such as drug protein marker response, the significance of cell population changes, and changes in cell morphology. To improve BASIN's accessibility and transparency we implemented it in R using Shiny framework and provided both an online trial version and a customizable offline version. We also have a batch version to run on datasets with hundreds of biomedical images. BASIN workflows consist of five core modules including image upload, feature extraction, statistical analysis, visualization, and report generation. © 2022 IEEE.

关键词： Machine learning

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Deepfake Image Detection using Deep Learning

Deepfake Image Detection using Deep Learning

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Uttam Upendra Hegde Anusha M S N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Deepfake images are generated by modifying existing visuals and are frequently exploited in harmful ways. When executed proficiently, these images can be almost indistinguishable from authentic ones. The increasing development of deep learning techniques has largely fueled the increase in deepfake content. While numerous techniques are available for creating deepfake images, the most commonly employed are GANs and autoencoders. This paper presents a way to make deepfake detection more accurate by using a combination of the YOLOv8 model and a Recurrent Neural Network (RNN). YOLOv8 extracts spatial details from the images, while the RNN identifies temporal patterns and subtle irregularities across image frames, signaling potential manipulation. This methodology presents an efficient solution for identifying deepfake images and reducing their misuse.

关键词： YOLO Deep learning Deepfakes Adaptation models Analytical models Visualization Recurrent neural networks Accuracy Computational modeling Faces

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The Effects of Selected Preferred Music on Perceived Emotions through Audiovisual Stimuli

The Effects of Selected Preferred Music on Perceived Emotion...

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Big Data, Cloud Computing, Data science & engineering (BCD)

作者： Thunrada Thaiwong Makoto Fukumoto Graduate School of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan Dept. of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

Music surrounds us, and there is no denying that music in visual media can shape and evoke emotions. Yet, understanding how musical preference influences emotions through audio and visual stimuli remains an important task. To address this task, we investigated the role of musical preference effect on perceived emotions induced through music and visual stimuli (i.e., animation), using a 7-point scale for valence-arousal ratings and physiological responses in electroencephalogram (EEG) band power. The perceived emotions are categorized into four states: happiness, calmness, fear, and sadness. One emotional state contains 4 sessions: (1) Preferred Music, (2) Unfamiliar Music, (3) Preferred Music+Animation, and (4) Unfamiliar Music+ Animation. Behavior-wise, the rating results showed that the presence of preferred music resulted in higher perceived valence ratings, particularly in happiness. However, no stimulus had a significant effect on perceived arousal ratings and satisfaction ratings. Physiologically, the EEG indexes showed that the presence of preferred music appears to affect an increase in alpha power across various emotions except sadness, whereas unfamiliar music seems to affect beta power, particularly in happiness and calmness. Overall, these findings supported that musical preference is an affective factor reflecting the levels of valence and alpha power in EEG. Nevertheless, our findings did not confirm a significant difference between only preferred music and combining it with animation. Interestingly, we also found that participants were more likely to be attracted to and perceive positive (i.e., happiness and calmness) emotions easily through preferred music and/or visual stimuli than negative (i.e., fear) emotions.

关键词： Visualization Cloud computing Shape Music Media Data science Animation Physiology Electroencephalography Indexes

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Cyber Complaint Automation System

Cyber Complaint Automation System

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Advanced Computing and Communication Technologies for High Performance Applications

作者： Anand V Prabhu MJ Jefiya Joel Denny Joseph Tisa Sunny Cerene Mariam Abraham Computer Science and Engineering dept. of Artificial Intelligence and Data Science

Due to the Internet's quick spread and the digitali-sation of commercial activity, cybercrime has risen dramatically. computers or data are the intended victim of cybercrime. This paper is about “Cyber Complaint Automation System”. The current cyber cell of Kerala police has a time comsuming approach. Complaint can be given in two ways. A written complaint can be given to cyber police station. Second option is to use cyber crime reporting portal. In this portal user has to undergo four different steps to sucessfully register the complaint. These complaints are stored in a database and manually classified into respective departments. This methodology of a cyber complaint takes a great deal of effort and time. The time wasted on current system helps the criminal in covering his track. As a result, the victim's time and money are squandered, and police receive criticism. We are working to create a software that will drastically shorten the time it takes to handle a complaint in an effort to remedy this scenario. Our system automates the classification process and prioritize emergency complaints. Thus helps the police to apprehend the criminal faster.

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Differential Diagnosis of Lung Disease through Quantitative Lung Ultrasound Spectroscopy, an in vivo clinical study over 114 patients

Differential Diagnosis of Lung Disease through Quantitative ...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Mento, Federico Perpenti, Mattia Barcellona, Giuliana Perrone, Tiziano Demi, Libertario Dept. of Information Engineering and Computer Science Ultrasound Laboratory Trento University of Trento Trento Italy Emergency and Urgency Department Humanitas Gavazzeni Bergamo Bergamo Italy

ISBN: (纸本)9798350346459

Lung ultrasound (LUS) represents an important imaging tool to evaluate the state of the lung surface. However, it is mainly based on the visual interpretation of imaging artifacts, especially the vertical ones. This subjective interpretation leads to poorly reproducible approaches, and, thus, qualitative diagnoses. Therefore, in this study, we present a quantitative multifrequency analysis based on the parameterization of vertical artifacts. Specifically, radiofrequency (RF) data were acquired from 114 patients affected by different diseases using a linear and a convex probe, connected to the ULtrasound Advanced Open Platform (ULA-OP). The vertical artifacts observed in patients affected by pneumonia, cardiogenic pulmonary edema (CPE), and pulmonary fibrosis (PF) were extracted and analyzed. Specifically, the significance of three features (native frequency, bandwidth, and total intensity) in discriminating these diseases was evaluated. The results show how the total intensity seems significant in differentiating PF from the other two diseases (p-value © 2023 IEEE.

关键词： Ultrasonics

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Evaluating Large Language Models for Automatic Register Transfer Logic Generation via High-Level Synthesis

arXiv

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arXiv 2024年

作者： Swaroopa, Sneha Mukherjee, Rijoy Debnath, Anushka Chakraborty, Rajat Subhra The Dept. of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India The Dept. of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India

The ever-growing popularity of large language models (LLMs) has resulted in their increasing adoption for hardware design and verification. Prior research has attempted to assess the capability of LLMs to automate digital hardware design by producing superior-quality Register Transfer Logic (RTL) descriptions, particularly in Verilog. However, these tests have revealed that Verilog code production using LLMs at current state-of-the-art lack sufficient functional correctness to be practically viable, compared to automatic generation of programs in general-purpose programming languages such as C, C++, Python, etc. With this as the key insight, in this paper we assess the performance of a two-stage software pipeline for automated Verilog RTL generation: LLM based automatic generation of annotated C++ code suitable for high-level synthesis (HLS), followed by HLS to generate Verilog RTL. We have benchmarked the performance of our proposed scheme using the open-source VerilogEval dataset, for four different industry-scale LLMs, and the Vitis HLS tool. Our experimental results demonstrate that our two-step technique substantially outperforms previous proposed techniques of direct Verilog RTL generation by LLMs in terms of average functional correctness rates, reaching score of 0.86 in pass@1 metric. Copyright © 2024, The Authors. All rights reserved.

关键词： C++ (programming language)

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Estimation of Relative Position of Drone using Fixed Size QR Code

Estimation of Relative Position of Drone using Fixed Size QR...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Kang, Tae-Won Choi, Yong-Sik Jung, Jin-Woo Dongguk University Dept. of Artificial Intelligence Seoul Korea Republic of Dongguk University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Inventory management is very important in any industry. In the past, people were directly involved in inventory management. However, this method is time consuming and requires a lot of labor. Therefore, in recent years, inventory management methods using drones are increasing. In this paper, we introduce an inventory management system using RFID and QR code, and a method of estimating the relative position of a drone based on the QR code, which is essential in the system. As a result of the experiment, when the center point of the QR code and the center point of the camera coincide, the error rate was very small, 0-1%. When the center points do not coincide, an error rate of up to 4% occurred. © 2022 IEEE.

关键词： Drones

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