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International Conference on Practical Applications of Agents and Multi-Agent systems, PAAMS 2023

作者： Marco-Detchart, C. Rincon, J.A. Julian, v. Carrascosa, C. Camino de Vera s/n Valencia46022 Spain Camí de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031375927

image processing techniques have become increasingly popular in plant disease classification. However, one of the major challenges in this field is accurately identifying and classifying different diseases based on plant images. Pre-processing techniques such as smoothing and sharpening can play a crucial role in improving the accuracy of disease classification. These techniques can enhance the quality of the images and reduce noise, making it easier for machine learning algorithms to extract meaningful features from the images. In this context, these techniques can significantly improve the overall accuracy of plant disease classification systems. This paper aims to explore the potential of preprocessing techniques such as smoothing and sharpening in enhancing the quality of plant images for disease classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Introduction to optical coherence elastography : tutorial

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND vISION 2022年第3期39卷 418-430页

作者： Singh, Manmohan Zvietcovich, Fernando Larin, Kirill, v Univ Houston Biomed Engn Houston TX 77204 USA Pontificia Univ Catolica Peru Dept Engn Lima 15088 Peru Baylor Coll Med Mol Physiol & Biophys Houston TX 77030 USA

Optical coherence elastography (OCE) has seen rapid growth since its introduction in 1998. The past few decades have seen tremendous advancements in the development of OCE technology and a wide range of applications, including the first clinical applications. This tutorial introduces the basics of solid mechanics, which form the foundation of all elastography methods. We then describe how OCE measurements of tissue motion can be used to quantify tissue biomechanical parameters. We also detail various types of excitation methods, imaging systems, acquisition schemes, and data processing algorithms and how various parameters associated with each step of OCE imaging can affect the final quantitation of biomechanical properties. Finally, we discuss the future of OCE, its potential, and the next steps required for OCE to become an established medical imaging technology. (c) 2022 Optica Publishing Group

关键词： Analytical techniques Clinical applications image resolution Imaging systems In vivo imaging Medical imaging

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Design and Implementation of a Tilapia (Oreochromis niloticus) Fingerling Counter Using Otsu Thresholding and Morphological Transformation Techniques 11

Design and Implementation of a Tilapia (Oreochromis niloticu...

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11th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Portea, Tom Jairus v. De La Fuente, Maricris I. Medrano, Mae Z. Felices, Farah Faye P. Naldo, Ian Jake A. Loquias, Rizza T. Tabal, Keith Marlon R. College of Engineering & Architecture Camarines sur Polytechnic Colleges Camarines Sur Philippines

ISBN: (纸本)9798350355314

Many different image processing techniques have been developed and implemented in counting systems for livestock and fisheries applications. These existing systems often require sophisticated hardware and computational resources, leading to an increase in the financial costs associated with their production. In this paper, an innovative Tilapia (Oreochromis niloticus) fingerling counting system is described. The system is comprised of low-cost computer vision-based hardware and software that integrates Otsu thresholding and morphological transformation techniques. These techniques derive contours from segmented regions generated by the formulated algorithms. It uses a Raspberry Pi 4B single-board computer platform in conjunction with a Raspberry Pi Camera Module version 1.3 to capture images. The performance of the manual process and the proposed method were evaluated by determining its mean squared error using a ground truth as a reference. Based on the tests performed, it was revealed that the proposed automatic counting system resulted in a better performance than the manual counting method. © 2024 IEEE.

关键词： Mean square error

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Auto-Detection of Handwritten Text Documents using Improved image processing Technique 15

Auto-Detection of Handwritten Text Documents using Improved ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srinivasan, M. Radha, K. Priya, v. Basha, Shaik Mahaboob Ravi, S. Bannari Amman Institute of Technology Department of Eee Sathyamangalam India Dr.Mahalingam College of Engineering and Technology Department of Cse Coimbatore Pollachi India Department of Microbiology TamilNadu Chennai India School of Engineering Department of Ece Andhra Pradesh Tirupati India Saveetha University Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798350370249

Automobile detection of handwritten text files is vital in document control systems. It includes the process of automatically figuring out and extracting handwritten text from pictures and converting it into editable virtual text. This method is challenging due to the variations in handwriting styles and the complexity of handwritten textual content reputation. Current methods for auto-detection depend upon conventional picture processing strategies, including thresholding, segmentation, and characteristic extraction, which need to be more robust to handle various handwriting patterns. A stepped-forward photograph processing method for automobile detection of handwritten textual content documents is proposed to deal with this issue. The technique uses deep mastering algorithms, including convolutional neural networks (CNNs), to robotically research the features of handwritten textual content and classify it appropriately. The proposed technique additionally employs facts augmentation techniques to boost the dataset's variety, making it more robust for variations in handwriting styles. Experimental consequences on an extensive handwritten textual content file dataset show that the proposed technique outperforms traditional techniques, accomplishing a better accuracy fee. The advantages of this method encompass its adaptability to one-of-a-kind handwriting styles, its velocity, and its potential to deal with handwritten textual content in numerous languages © 2024 IEEE.

关键词： Appropriately Augmentation Consequences Experimental Handwritten Mastering

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Development of Intelligent Nutrition Surveillance System with OpenAI API 4

Development of Intelligent Nutrition Surveillance System wit...

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4th International Workshop of IT-Professionals on Artificial Intelligence, ProfIT AI 2024

作者： Yevtushenko, vadym Rukkas, Kyrylo Chumachenko, Tetyana National Aerospace University "Kharkiv Aviation Institute" Vadym Manko str. 17 Kharkiv61070 Ukraine V.N. Karazin Kharkiv National University Freedom sq. 4 Kharkiv61001 Ukraine Kharkiv National Medical University Nauky ave. 4 Kharkiv61001 Ukraine

Nutrition surveillance is crucial in monitoring public health by identifying dietary deficiencies and tracking nutritional trends. Traditional systems often struggle with data collection and analysis challenges, particularly in crises. Artificial intelligence (AI) offers promising solutions for improving the efficiency and accuracy of such systems. This study aims to develop an intelligent nutrition surveillance system that leverages the OpenAI API to provide personalized dietary recommendations, improve real-time data processing, and enhance nutrition management. The system integrates natural language processing (NLP) and machine learning models for food recognition and nutrient estimation. Data is collected through a user-friendly interface, and personalized meal plans are generated based on user inputs. The system's performance was tested using a combination of manual data entry and food image recognition, with user feedback guiding further refinements. The OpenAI-based system successfully provided real-time, personalized nutrition plans. The NLP model accurately processed user queries, while the food recognition model performed well with simple meals and struggled with complex dishes. User satisfaction was generally high, but some data input guidance and food recognition accuracy improvements were noted. This research demonstrates the practical application of AI in nutrition surveillance, particularly in resource-constrained settings. The system's ability to generate personalized recommendations using real-time inputs represents a significant advancement in public health technology. The study presents a scalable and adaptable framework for integrating AI into nutrition surveillance, showing strong potential for further application in individual and population health. Future research should focus on refining image recognition algorithms and incorporating automated data collection methods to enhance accuracy and applicability. © 2024 Copyright for this paper by it

关键词： Artificial Intelligence nutrition surveillance OpenAI personal assistance public health informatics

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Object Distinction in Noisy images

Object Distinction in Noisy Images

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2022 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2022

作者： volkov, v.Yu. Saint-Petersburg State University of Aerospace Instrumentation Saint-Petersburg Russia

ISBN: (纸本)9781665406352

The aim of the work is to develop and study an adaptive algorithm for selection and differentiation of isolated compact objects by their shape. For the analysis, monochrome images inherent in remote sensing systems were considered, the proposed multi-threshold approach is based on the formation of a set of binary slices that are used for morphological processing by selecting objects, analyzing their geometric characteristics and subsequent selection. One of the problems is to establish the connection of each pixel of the image with a specific object on a sequence of binary slices. To solve this problem, the rate of change in the area of the object with an increase in the threshold and fragmentation of the object due to the percolation effect are analyzed. Another problem is related to the selection and use of geometric characteristics of the object to adapt the threshold level. What is new is the use of object selection results for image segmentation, which allows you to set lower-level thresholds without increasing interference. An example of distinguishing objects in the form of a disk and a square appearing on a noise Gaussian background is considered. The potential distinguishing characteristics that coincide with the simulation results are calculated. The possibility of selection and differentiation of these objects using multi-threshold processing is investigated. Examples of distinguishing compact objects in real images obtained by on-board surveillance systems are presented. © 2022 IEEE.

关键词： Adaptive algorithms

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AINoC: New Interconnect for Future Deep Neural Network Accelerators 16th

AINoC: New Interconnect for Future Deep Neural Network Accel...

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16th International Workshop on Design and Architecture for Signal and image processing (DASIP) held jointly with the 18th HiPEAC Conference

作者： Krichene, Hana Prasad, Rohit Mouhagir, Ayoub Univ Paris Saclay List CEA F-91120 Palaiseau France

ISBN: (纸本)9783031299698;9783031299704

Data exchanges can be significant in the Deep Neural Network (DNN) algorithms. The interconnection between computing resources can quickly have a substantial impact on the overall performance of the architecture and its energy efficiency. Similarly, access to the different memories of the system, with potentially high data sharing, is a critical point. To overcome these problems, in this paper, we propose a new interconnect network, called AINoC, for future DNN accelerators, which require more flexibility and less power consumption to facilitate their integration into artificial intelligence (AI) edge systems. AINoC is based on (1) parallel routing that ensures communication/computation overlap to improve performance and (2) data reuse (filters, image inputs, and partial sums) to reduce multiple memory accesses. In the experiment section, AINoC can speedup LeNet5 convolution layers by up to 71.74x in latency performance w.r.t. a RISC-v-based CPU and also speedup MobileNetv2 convolution layers by up to 2.35x in latency performance w.r.t. a dataflow architecture featuring row-stationary execution style. AINoC provides any-to-any data exchange with wide interfaces (up to 51.2 GB/s) to support long bursts (up to 384 flits/cycle with packed data, i.e., 3 * 8-bit data in a 32-bit wide datapath) while executing LeNet5 and MobileNetv2. AINoC supports flexible communication with many multicast/broadcast requests with non-blocking transfers. Parallel communication in AINoC can provide up to 128x more throughput (flits/cycle) and bandwidth (GB/s), using parallel routing with respect to single-path routing while executing convolution layers of LeNet5 and MobiletNetv2.

关键词： On-Chip Interconnect Network-on-Chip DNN CNN dataflow execution parallel processing

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Design of computing granularity configurable processor based on RISC-v extended instruction 16

Design of computing granularity configurable processor based...

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16th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2022

作者： He, Bin Yu, Ning-Mei Xu, Meng Wang, Xing-Jia Xi'an University of Technology School of Automation and Information Engineering Xi'an710000 China

ISBN: (数字)9781665469067

ISBN: (纸本)9781665469067

Aiming at the problems of long operation time and poor flexibility of embedded processor, this paper proposes a structure of configurable execution unit based on RISC-v extended instruction set for computing granularity. The structure sets up basic parallel computing units and configuration registers, which can realize a large number of parallel operations such as multiplication, addition, or higher abstractions similar to convolutional neural networks by software configuration. This paper completed the design of this structure and integrated it into a four-stage pipelined RISC-v processor. The white blood cell images collected by lensless system are used to test various image algorithms. The experimental results show that the processor speed designed in this paper is 5.95 times to 22.21 times higher than that of the ordinary pipeline processor, and the number of instructions required is reduced by 1.16 times to 7.49 times. Therefore, the processor has a certain improvement in processing speed and flexibility, which is of great significance for the implementation of various algorithms in embedded systems. © 2022 IEEE.

关键词： Pipeline processing systems

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Design and Development of Smart System for the Identification and Diversion of Wild Animal in Agricultural Field 10

Design and Development of Smart System for the Identificatio...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Singh, R. Lal Raja Thirumalaivasan, v. Kumar, N. Navin Adharsh, R. KIT-Kalaignarkarunanidhini Institute of Technology Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350384369

As human populations continue to expand, conflicts between agriculture and wildlife have become increasingly prevalent. The 'Design and Development of a Smart System for the Identification and Diversion of Wild Animals in Agricultural Field' addresses this critical issue by proposing an innovative solution that leverages cutting-edge technology to minimize crop damage and protect both agricultural livelihoods and wildlife populations. This project centers on the creation of a comprehensive smart system that integrates various sensors, including cameras, infrared detectors, and machine learning algorithms, to accurately identify and track wild animals entering agricultural fields both Day-time and Night-time. By using 360 degree night vision camera, samples are taken continuously. With the samples, Raspberry pi compare it with the database to detect the animal By which it could be activate the irritating frequency or high beam for certain wild animal. Because of using raspberry Pi the image processing and processing speed will be high. Upon detection, the system employs real-time data analysis to categorize the species and assess the potential threat to crops. Subsequently, it triggers a non-harmful diversion mechanism, such as flashing lights, sounds, or automated barriers, to deter the animals from causing damage. The objectives of this project include reducing crop losses, mitigating Conflicts between people and wildlife, as well as encouraging agriculture and wildlife coexistence. Moreover, it aims to provide valuable data for conservation efforts and wildlife monitoring. By implementing this Smart System, we anticipate not only safeguarding agricultural yields but also contributing to the conservation of wildlife populations. The research and technology behind this project have the potential to serve as a model for sustainable cohabitation between humans and the world. © 2024 IEEE.

关键词： Differential amplifiers

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 International Conference on Distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, v. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： Computerized tomography

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