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Harnessing AI and Imaging Innovations for Enhanced Detection and Diagnosis of Age-Related Macular Degeneration: An In-Depth Review

Harnessing AI and Imaging Innovations for Enhanced Detection...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Sana Swaroop Manda Rama Narasinga Rao Department of Computer Science & Engineering GITAM University Visakhapatnam Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Age-related macular degeneration (AMD) is a leading cause of irreversible vision impairment among the elderly population worldwide, affecting over 196 million people globally. this study delves into innovative approaches for improving the detection and management of AMD through advanced technological solutions. To investigate the impact of ultraviolet (UV) and blue light exposure on AMD, the research analyzes their contribution to oxidative stress and retinal damage and evaluates potential protective measures or interventions to mitigate these effects. By leveraging state-of-the-art machine learning algorithms and advanced image processing techniques, the research aims to enhance the precision and efficiency of AMD diagnosis. the statistical burden of AMD underscores its significant impact on global health, with projections indicating a rising prevalence due to aging populations, lifestyle factors, and increasing digital screen use among younger generations. Effective management hinges on early detection and accurate monitoring of AMD biomarkers, which these methodologies seek to facilitate. Experimental evaluations demonstrate promising outcomes in diagnostic accuracy and scalability, highlighting the potential for widespread adoption in clinical practice. Furthermore, these advancements contribute to broader efforts in global eye health by offering scalable, AI-driven solutions that can improve patient outcomes and streamline healthcare workflows. By addressing the complexities of AMD diagnosis, this research supports healthcare providers in delivering timely interventions and personalized care, ultimately reducing the burden of AMD-related vision loss on individuals and healthcare systems.

关键词： Macular degeneration Training Technological innovation Accuracy Medical services Streaming media signal processing Ophthalmology Stress Stress measurement

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Audio Classification through MFCC features using RNN algorithm

Audio Classification through MFCC features using RNN algorit...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Kilaru Devi Bhargavi V. Deepa I. Harshitha Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Recurrent Neural Network (RNN) algorithm, is renowned for its effectiveness in object localization and classification tasks within the realm of computer vision. this study combines RNN capabilities with the analysis of Mel-Frequency Cepstral Coefficients(MFCC) features, which have demonstrated effectiveness in capturing unique audio signal characteristics. Mel-Frequency Cepstral Coefficients (MFCCs) serve as a compact representation of the spectral content of audio signals, enabling the identification of distinctive features crucial for detecting anomalies introduced during the generation of deepfake audio. the Recurrent Neural Network (RNN) algorithm is applied to these MFCC features. RNN excels in tasks such as image recognition, object detection, and semantic segmentation. Previous research in the field of audio forensics has explored various methodologies for detecting manipulated audio content. While some approaches focus on signal processing techniques, others employ machine learning algorithms. MFCCs have consistently demonstrated their efficacy in capturing audio features relevant to authenticity. the proposed approach enhances the detection of fake audio, a critical challenge in the era of advanced digital media manipulation. By combining the strengths of MFCC features and the RNN algorithm, the model aims to learn and identify intricate patterns associated with genuine audio, enabling accurate differentiation between authentic and manipulated audio sources. By adapting RNN to analyze MFCC features, the model gains the capability to discern intricate patterns associated with genuine audio, thereby distinguishing between authentic and manipulated audio sources.

关键词： Adaptation models Technological innovation Recurrent neural networks Machine learning algorithms Semantic segmentation signal processing algorithms signal processing Feature extraction Classification algorithms Mel frequency cepstral coefficient

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Improved Super-resolution Network based on SRCNN 21

Improved Super-resolution Network based on SRCNN

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Proceedings of the 2021 4th International conference on Algorithms, Computing and Artificial Intelligence

作者： Bin Yang Kai Xie Hongbo Pan Yuyan Chen Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipment Beijing Institute of Graphic Communication China School of Information Engineering Beijing Institute of Graphic Communication China

ISBN: (纸本)9781450385053

the process of recovering high-resolution images from one or more degraded low-resolution images is known as super-resolution reconstruction. the present super-resolution (SR) deep learning technique has certain limitations, because the deeper the network layer, the higher the training cost and time consumption. this paper, based on SRCNN, revamped the three-step reconstructed process of SRCNN by including VGG and residual ideas. Enhancing its network structure by utilizing subpixel convolution approach which can replace the step of image preprocessing and upscale in SRCNN algorithm. When comparing the reconstructed pictures of Set5, Set14, BSDS100, Urban100, and Manga109 data sets, the revised SRCNN structure outperforms the original SRCNN by 1.1dB in Peak signal to Noise Ratio (PSNR) and 0.026 in structural similarity (SSIM). When compared to other popular SR network except for SRCNN, the improved reconstructed network has fewer network layers, is easier to train, and produces higher reconstructed image quality than the original SRCNN.

关键词： Subpixel convolution VGG Residual Structure Super-resolution

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Ultrasound Beamforming: Exploring Cloud-Native and Edge Computing Solution

Ultrasound Beamforming: Exploring Cloud-Native and Edge Comp...

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Technological Advancements in Computational Sciences (ICTACS), International conference on

作者： Hamza Hadri Abderahhim Fail Mohamed Sadik Amine Essaken NEST Research Group LRI Lab ENSEM of Hassan II University Casablanca Morocco ENSEM of Hassan II University Casablanca Morocco

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Ultrasound imaging is pivotal in modern medicine for its non-invasive visualization of anatomical structures. How- ever, traditional systems face challenges in scalability, flexibility, and cost-effectiveness due to centralized processing units. this paper introduces a novel cloud-native architecture tailored for ultrasound imaging, focusing on the computationally intensive beamforming process. the architecture integrates on-premise edge-computing acquisition probes with cloud-based processing, leveraging edge computing to enhance efficiency and responsive- ness. Initial data preprocessing at the edge optimizes bandwidth use before secure transmission to the cloud via dedicated connections. In the cloud, real-time data ingestion through AWS Kinesis supports scalable processing using containerization technologies. High-performance computing languages ensure efficient beam- forming, improving diagnostic accuracy and operational efficacy. the architecture's scalability, cost-effectiveness, and integration potential for AI-driven diagnostics are discussed, highlighting its transformative impact on ultrasound imaging practices and healthcare delivery.

关键词： Cloud computing Ultrasonic imaging Array signal processing image edge detection Scalability Computer architecture Medical services Transforms Probes Edge computing

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Heating Energy Performance Assessment using KNN Classification method: Investigating Test Size, Neighbor Parameter, and Class Count Effects

Heating Energy Performance Assessment using KNN Classificati...

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International conference on image and signal processing and their applications (ISPA)

作者： Aissa Boudjella Manal Yasmine Boudjella Chuang Liu Dept. of Applied Mathematics Samarkand International University of Ttechnology Samarkand Uzbekistan Dept. Electrical Engineering Bircham International University Miami USA Dept. of Physics University of Sciences and Technology USTO Oran Algerie Dept. of Thermal Engineering Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

We analyze the energy requirements for assessing the heating load of building systems using the k-Nearest Neighbor (KNN) method for classification. Unsupervised transformations of the dataset are employed in learning algorithms. Two distinct data representation classes are generated separately from the original dataset based on captured heating load magnitudes. the unsupervised transformations of the dataset yield 4 and 8 classes, *** evaluate performance, various test and training sizes are employed, comparing measurement outcomes and predicting class label descriptions for two dataset representations as the parameter for the nearest neighbor (k) ranges from 1 to 20. Results obtained from the simulation indicate that accuracy is influenced by factors such as test size, the k parameter, and dataset class count. the training and test accuracy rates reach maximum values of 95% and 85%, respectively. Keeping k constant, the test size has a negligible impact on accuracy. the classes exhibit three critical regions: 1) Region I, where accuracy fluctuates with increasing k in the interval [1]-[5], and 2) Region II, where accuracy declines with increasing k in the interval [5]-[10]. In Region III k [10]-[20], accuracy remains relatively constant. While reducing the number of classes may result in a slight improvement in accuracy by simplifying the classification task, the trade-offs and complexities of increasing the number of classes for data representation should be carefully considered. Although this approach can lead to a more flexible design, it may slightly reduce accuracy.

关键词： Heating systems Training Shape Buildings Machine learning signal processing Size measurement

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Low Power signal processing via Approximate Multiplier for Error-Resilient applications

Low Power Signal Processing via Approximate Multiplier for E...

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IEEE International conference on Industrial and Information Systems

作者： Bharat Garg G K Sharma ABV-Indian Institute of Information Technology and Management Gwalior India

the exponentially enlarging usability and mushrooming features in the portable hand-held devices such as cellphone appeal for the highly energy-efficient designs as the user cannot tolerate power famished devices rather can manage with tiny degraded quality. the multimedia applications such as image/video processing are compute-intensive and require efficient designs which can be achieved by approximate designs with the feeble accord in accuracy. As multiplication is the most frequently used and highly energy-consuming arithmetic operation, in this paper, a low power approximate multiplier architecture (AMA) is proposed that significantly improves the design metrics. We evaluate the proposed AMA with various quality metrics and design an AMA embedded Gaussian smoothing filter (GSF). the simulation results show that AMA reduces 81.5% power consumption over accurate and 19.17% energy over the well-known approximate multiplier architecture. Further, the AMA embedded GSF reduces 6.2% area and 7.53% power consumption over the best known approximate multiplier embedded GSF.

关键词： Adders Computer architecture Approximation algorithms Algorithm design and analysis Smoothing methods Delays Algorithm design and analysis smoothing methods adders Computer Architecture Approximation algorithms Multiplying circuits Approximate energy-efficient design delay hand-held equipment arithmetic operation

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Controlling the Position and Velocity in Space of the Quad-Rotor UAV *** Using Predictive Functional Control and image processing in Open CV

Controlling the Position and Velocity in Space of the Quad-R...

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2012 4th International conference on signal processing Systems(ICSPS 2012)

作者： Anusha Gururaj Jamkhandi Saee Tulpule Ashvini Chaturvedi Jean-Noel Charvet National Institute of Technology Ecole Catholique d'Arts et Metiers

the aim of this work is to use Predictive Functional Control (PFC) to make the Parrot Drone follow a red ball. the Drone has an in-built proportional-integral-derivative (PID) controller in its software development kit to control its motion. However, large time delays and high-order dynamics are the common dynamic characteristics that are difficult to control using PID technique. this work aims to use PFC instead of PID control due to its shortcomings. We begin with developing an equivalent first order process model for Drone motors and its PID controllers by analyzing the Euler angles (pitch, roll and yaw) and the velocities in the X and Y directions. the Drone is then made to follow a red ball for which the coordinates of its centre are determined through image processing using Hough circles in Open CV and subsequently these coordinates are passed to the PF controller. the X and Y positions are controlled by the PFC by manipulating pitch and roll angles respectively. the results obtained are quite promising and thereby an attempt is made to overcome the limitations of PID controllers to a significant degree by using PF controllers.

关键词： proportional-integral-derivative control, predictive functional control, euler angles, hough circles, set point, manipulated variable, controlled variable

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A Joint Dynamic Domain Adaptation Module and Multi-incentive-aware Based Model for Sentiment Analysis of Autism Paintings

A Joint Dynamic Domain Adaptation Module and Multi-incentive...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jiayin Min The Experimental High School Attached to Beijing Normal University Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the development of society, the number of autistic children is increasing, and there is growing concern about their education and development. Autistic children often have difficulties in language expression, social interaction and emotional understanding. In this context, finding new ways to understand the emotions and needs of autistic children has become particularly important. By analyzing the drawings of autistic children and using deep learning models to mine the emotions and information embedded in them, we can provide educators and parents with a more intuitive and in-depth way to understand the inner world of autistic children. As a non-verbal form of expression, paintings can help autistic children release emotions and express their inner thoughts and feelings. Firstly, based on psychological research results, we propose a new image emotion classification network based on multi-incentive awareness, which consists of three phases, i.e., incentive selection (S), feature extraction (O) and emotion prediction (R). In the first phase, in this paper, we select special emotion excitations (color, object, face) by combining existing target detection methods in deep learning. Subsequently, we design three specific networks to extract different emotion features from multiple incentives synchronously, i.e., global network, semantic network, and expression network. Finally, the model combines all the above features for image emotion prediction. Comparative experiments demonstrate that the accuracy of our constructed sentiment analysis model outperforms existing algorithms. After training the above multi-incentive-aware image sentiment analysis model, we design a transfer learning model to map the features learned by the model on the regular image dataset to the drawing dataset of autistic patients. We perform multi-representation feature extraction using a hybrid structure, which consists of multiple sub-structural feature extraction units. At the same time we us

关键词： Deep learning Analytical models Adaptation models Sentiment analysis image color analysis Transfer learning Psychology Predictive models Feature extraction Painting

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AIM 2020 Challenge on Real image Super-Resolution: Methods and Results 16th

AIM 2020 Challenge on Real Image Super-Resolution: Methods a...

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Workshops held at the 16th European conference on Computer Vision, ECCV 2020

作者： Wei, Pengxu Lu, Hannan Timofte, Radu Lin, Liang Zuo, Wangmeng Pan, Zhihong Li, Baopu Xi, Teng Fan, Yanwen Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Xie, Tangxin Cao, Liang Zou, Yan Shen, Yi Zhang, Jialiang Jia, Yu Cheng, Kaihua Wu, Chenhuan Lin, Yue Liu, Cen Peng, Yunbo Zou, Xueyi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Ahn, Keon-Hee Kim, Jun-Hyuk Choi, Jun-Ho Lee, Jong-Seok Zhao, Tongtong Zhao, Shanshan Han, Yoseob Kim, Byung-Hoon Baek, JaeHyun Wu, Haoning Xu, Dejia Zhou, Bo Guan, Wei Li, Xiaobo Ye, Chen Li, Hao Zhong, Haoyu Shi, Yukai Yang, Zhijing Yang, Xiaojun Li, Xin Jin, Xin Wu, Yaojun Pang, Yingxue Liu, Sen Liu, Zhi-Song Wang, Li-Wen Li, Chu-Tak Cani, Marie-Paule Siu, Wan-Chi Zhou, Yuanbo Umer, Rao Muhammad Micheloni, Christian Cong, Xiaofeng Gupta, Rajat Ahn, Keon-Hee Kim, Jun-Hyuk Choi, Jun-Ho Lee, Jong-Seok Almasri, Feras Vandamme, thomas Debeir, Olivier Sun Yat-sen University Guangzhou China Harbin Institute of Technology University Harbin China Computer Vision Lab ETH Zurich Zurich Switzerland Baidu Research Silicon Valley United States Baidu Incorportation Silicon Valley United States China Electronic Technology Cyber Security Co. Ltd. Beijing China Guangdong OPPO Mobile Telecommunications Corp. Ltd. Dongguan China NetEase Games AI Lab Beijing China Noah’s Ark Lab Huawei Beijing China Co. Ltd. Shanghai China Beijing China Yonsei University Seodaemun-gu Korea Republic of Dalian Maritime Univerity Dalian China New Mexico United States Daejeon Korea Republic of Seattle United States Peking University Beijing China Jiangnan University Jiangnan China Karlsruher Institut fuer Technologie Karlsruher Germany Tongji University Tongji China Guangdong University of Technology Tongji China University of Science and Technology of China Hefei China Palaiseau France Center of Multimedia Signal Processing The Hong Kong Polytechnic University Hong Kong China Fuzhou University Fuzhou Fujian China University of Udine Udine Italy Indian Institute of Technology Khargapur India National University of Defense Technology Changsha China LISA Department Universie Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9783030670696

this paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. this challenge involves three tracks to super-resolve an input image for × 2, × 3 and × 4 scaling factors, respectively. the goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications. 452 participants were registered for three tracks in total, and 24 teams submitted their results. they gauge the state-of-the-art approaches for real image SR in terms of PSNR and SSIM. © 2020, Springer Nature Switzerland AG.

关键词： Optical resolving power

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4×4 Array Multiplier Using Transmission Gate Full Adder

4×4 Array Multiplier Using Transmission Gate Full Adder

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International conference for Convergence of Technology (I2CT)

作者： Nitesh Kumar Rathore Ankur Beohar Pallabi Sarkar Kavita Khare Pooran Singh Dept. of Electrical and Computer Engineering Mahindra University Hyderabad India School of Electrical and Electronics Engineering (SEEE) VIT Bhopal University Madhya Pradesh India Dept. of Electronic and Communication Engineering MANIT Bhopal Madhya Pradesh India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Multiplication is the most essential function carried out by the arithmetic and logic unit. the multiplier specifically has important applications in signal processing analysis, image processing analysis, and so on. the design of multipliers with high-performance properties such as fast speed, low power, small size, and minimal latency is of great interest in the research field. In this research, a 4×4 array multiplier using a transmission gate full adder has been designed. the full adder used in the design is a conventional CMOS full adder implemented using XOR, AND, OR gates with a load capacitance of 32F. In the architecture, the same 4×4 multiplier has been designed using semi-dynamic logic and has carried out different design parameters calculations and observations respectively along with process, voltage, and temperature variation (PVT) of 10%. When compared to Full Adder implementation using a semi-dynamic logic gate, Full Adder implementation utilizing a transmission gate imparts less latency. Each of the mentioned logic blocks is designed using LT-Spice software for 180nm technology.

关键词： image processing Voltage Logic gates Software Power dissipation Delays Adders

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