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Revolutionizing Medical Diagnostics: Exploring Creativity in AI for Biomedical Image Analysis

Revolutionizing Medical Diagnostics: Exploring Creativity in...

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： P. Krishnamoorthy Devulapally Swetha Pamidimukkala Sai Geetha K. Karunambiga Ramesh Kumar Ayyasamy Ajmeera Kiran PG Department of Computer Science Thiruthangal Nadar College Chennai Tamil Nadu India Department of Computer Science and Engineering Vaagdevi Engineering College Bolli Kunta Telangana India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of CSE Karpagam Institute of Technology Coimbatore Tamil Nadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Biomedical image analysis plays a crucial role in the early and accurate diagnosis of diseases, significantly impacting patient outcomes. This study presents an innovative approach to biomedical image analysis by integrating machine learning techniques with a user-friendly web interface. The system takes input images uploaded through the web interface and processes them using advanced machine learning algorithms implemented in Python. The goal is to automate the detection of diseases from these images, enhancing the efficiency and precision of diagnosis. Convolutional Neural Networks (CNNs), a specialized class of deep learning models designed for image analysis, are employed to learn and characterize disease-specific patterns. The proposed approach is evaluated using diverse biomedical images encompassing various diseases and conditions. Extensive experiments demonstrate the system's effectiveness, achieving high accuracy, sensitivity, and specificity in disease detection. Integrating machine learning with a web interface simplifies the diagnostic process and enables remote consultations, making healthcare services more accessible and timelier. In conclusion, this study presents a robust framework for automated biomedical image analysis, demonstrating the potential of machine learning and web technologies in revolutionizing healthcare diagnostics. The system's accuracy and user-friendly interface make it a valuable tool for healthcare professionals, contributing to early disease detection and improved patient care.

关键词： Accuracy Machine learning algorithms Image analysis Medical services signal processing Sensitivity and specificity Robots

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Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment

Using Ear-EEG to Decode Auditory Attention in Multiple-speak...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haolin Zhu Yujie Yan Xiran Xu Zhongshu Ge Pei Tian Xihong Wu Jing Chen Speech and Hearing Research Center School of Intelligence Science and Technology Peking University National Key Laboratory of General Artificial Intelligence Peking University Center for BioMed-X Research Academy for Advanced Interdisciplinary Studies Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements of electroencephalography (EEG) data. Most studies on AAD are based on scalp-EEG signals in two-speaker scenarios, which are far from real application. Ear-EEG has recently gained significant attention due to its motion tolerance and invisibility during data acquisition, making it easy to incorporate with other devices for applications. In this work, participants selectively attended to one of the four spatially separated speakers’ speech in an anechoic room. The EEG data were concurrently collected from a scalp-EEG system and an ear-EEG system (cEEGrids). Temporal response functions (TRFs) and stimulus reconstruction (SR) were utilized using ear-EEG data. Results showed that the attended speech TRFs were stronger than each unattended speech and decoding accuracy was 41.3% in the 60s (chance level of 25%). To further investigate the impact of electrode placement and quantity, SR was utilized in both scalp-EEG and ear-EEG, revealing that while the number of electrodes had a minor effect, their positioning had a significant influence on the decoding accuracy. One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93.1% in 1-second decoding window. The implementation code and database for our work are available on GitHub: https://***/zhl486/Ear_EEG_*** and Zenodo: https://***/records/10803261.

关键词： Electrodes Accuracy Neural activity signal processing algorithms Electroencephalography Spatial databases Decoding Recording Speech processing Software development management

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Development of signal processing algorithms for De-noising of ECG signals in Wearable Systems and Testing for Arrhythmia using CNN Model 3

Development of Signal Processing Algorithms for De-noising o...

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3rd IEEE International Virtual conference on Innovations in Power and advanced Computing Technologies, i-PACT 2021

作者： Shekhar, Aryan Hota, Shivam Mahalakshmi, P. Sharmila, A. School of Electrical Engineering VIT Vellore India

ISBN: (纸本)9781665426916

Wireless Data Communication is an emerging prospect of technology in the current world scenario. processing an ECG signal to drive out informative insights needs the signal to be noise free. If the noise persists, it can damage the complete signal and it cannot be used for healthcare processes. De-noising techniques are employed to reduce the noise levels in the signal such that it can be further used for diagnosis. The various types of noise in an ECG signal are: Powerline Interference, Electrode Contact noise, Muscle Contraction noise, Baseline Wander and Instrumentation noise. The paper will give a detailed explanation of the different types of noise and their reduction techniques implemented in MATLAB R2017a which are compared on various parameters. Further, using image dataset of normal and abnormal heartbeat, a CNN model is proposed which classifies the heartbeat from ECG signal as arrhythmia or normal with an accuracy of 92.62%. This algorithm will de-noise the signal as well as check for arrhythmia in a feasible and accurate way. © 2021 IEEE.

关键词： Diseases

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Intelligent ID System for the Industrial Internet of Things Using Machine Learning algorithms

Intelligent ID System for the Industrial Internet of Things ...

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International conference on advanced Systems and Electric Technologies (IC_ASET)

作者： BACHA Sawssen LIOUANE Noureeddine Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

The Industrial Internet of Things offers modern industrial firms numerous opportunities for significant growth. The Internet of Things technology utilized in the sector has developed into a large-scale network as a result of the increasing amount of data and devices. Industrial IoT networks are by nature susceptible to hacking and intrusions. Thus, in order to ensure the security of IIoT networks, intrusion detection system IDS development is essential. This study proposes an IDS to defeat a variety of cyberattacks in industrial Internet of things environments. The suggested approach reduces the dimension of the data characteristics and enhances the performance of anomaly identification by using the kernel principal component analysis technique. For binary classification, we use the kernel extreme learning machine to identify if the traffic flow is benign or attack. We use it to multiclass classification in order to classify the set of attacks based on the specific type of attack. The performance results are assessed and analyzed using the X-IloTDS dataset in order to prove the effectiveness of the suggested anomaly detection approach. The evaluation's findings show that the suggested anomaly detection strategy may greatly raise detection performance outcomes in terms of specificity, accuracy, sensitivity, and F1-score while also successfully increasing detection efficiency.

关键词： Training Machine learning algorithms Accuracy Sensitivity Security Computer crime Kernel

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A Review of Spectral Estimation in Underwater Acoustic signal processing

A Review of Spectral Estimation in Underwater Acoustic Signa...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Chao Peng International School of Microelectronics Dongguan University of Technology DongGuan China

ISBN: (纸本)9781665479691

This paper first introduces the direct method of classical power spectrum estimation, and then proposes two improved classical power spectrum estimation methods: Welch Method and MultiTaper (MTM) Method. Then the Maximum Entropy Spectral Estimation (based on Burg) in parametric spectral estimation is introduced. Then the power spectrum is extended in the spatial domain, and two kinds of spatial spectrum estimation methods, MUSIC Method and ESPRIT method, and their improved algorithms are introduced. All of the above methods have been simulated in Matlab environment. We have compared various spectrum estimation methods in detail, and analyzed and summarized their respective advantages and disadvantages. Finally, the paper points out the shortcomings of current power spectrum estimation and the future development direction.

关键词： Estimation signal processing algorithms Process control signal processing Entropy Multiple signal classification Information management

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SAMO: Speaker Attractor Multi-Center One-Class Learning For Voice Anti-Spoofing

SAMO: Speaker Attractor Multi-Center One-Class Learning For ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Siwen Ding You Zhang Zhiyao Duan Columbia University New York NY USA University of Rochester Rochester NY USA

Voice anti-spoofing systems are crucial auxiliaries for automatic speaker verification (ASV) systems. A major challenge is caused by unseen attacks empowered by advanced speech synthesis technologies. Our previous research on one-class learning has improved the generalization ability to unseen attacks by compacting the bona fide speech in the embedding space. However, such compactness lacks consideration of the diversity of speakers. In this work, we propose speaker attractor multi-center one-class learning (SAMO), which clusters bona fide speech around a number of speaker attractors and pushes away spoofing attacks from all the attractors in a high-dimensional embedding space. For training, we propose an algorithm for the co-optimization of bona fide speech clustering and bona fide/spoof classification. For inference, we propose strategies to enable anti-spoofing for speakers without enrollment. Our proposed system outperforms existing state-of-the-art single systems with a relative improvement of 38% on equal error rate (EER) on the ASVspoof2019 LA evaluation set.

关键词： Training Representation learning Performance evaluation Codecs Speech coding Error analysis signal processing algorithms

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Preface 27th International conference on Circuits, Systems, Communications and Computers

Preface 27th International Conference on Circuits, Systems, ...

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Circuits, Systems, Communications and Computers (CSCC), International conference on

作者： Nikos Mastorakis Technical University of Sofia Sofia Bulgaria

This volume comprises the proceedings of the 27th International conference on Circuits, Systems, Communications and Computers (CSCC 2023). The conference provided a platform for researchers, engineers, academicians as well as industrial professionals to discuss research results and development activities in Circuits, Systems, Control, signal processing, Mathematical Methods and Techniques, Analytical Methods, Simulation Methods, advanced algorithms, Power Systems, Energy, Communications, Information Systems, Network Systems, Intelligent Systems, Forecasting Methods, Data Analysis, Computers in Sciences and Industry, Cyber Security and advanced Computer Applications, with participants from all over the world. The conference was held July 19-22, 2023 in Rodos, Rhodes Island, Greece. All chapters are self-contained and peer reviewed.

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Thickness Measurement of Alloy Steel Carburizing Layer Based on Laser Ultrasonic

Thickness Measurement of Alloy Steel Carburizing Layer Based...

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advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International conference on

作者： Xing Mao Dong Xu Shaotong Zhang Xiaocheng Wang Quan Yang Ruixiao Wang Wei Sun Rengfei Yu Institute of Engineering Technology University of Science and Technology Beijing Beijing China School of Mechanical Engineering and Automation Northeastern University Shenyang China Capital Engineering & Research Incorporation Limited Beijing China Hot Rolling Workshop Angang Steel Company Limited Anshan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Carburizing is one of the most widely used chemical heat treatment processes in the machinery manufacturing industry. The surface hardness, wear resistance and flexural fatigue strength of the workpiece after carburization is greatly improved. The thickness of the carburized layer is one of the main technical indicators to measure the quality of carburized parts. However, at present, the carburized shaft carburized layer thickness inspection is generally a contact method. In this paper, the thickness of the carburized layer of alloy steel was detected using Laser Ultrasonic (LU) non-destructive testing technology. First of all, using simulation signals to verify the effectiveness of sparse decomposition reconstruction and wavelet threshold denoising algorithm, and then the use of sparse decomposition and reconstruction combined with wavelet threshold denoising algorithm on the experimental LU signal denoising to extract the weak signal, obtained carburized layer thickness of alloy steel, accuracy of test results up to more than 95%. This study provides theoretical and data support for the development and application of on-line inspection equipment for carburized layers.

关键词： Ultrasonic variables measurement Noise reduction signal processing algorithms Measurement by laser beam Inspection Acoustics Time measurement Steel Thickness measurement Testing

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signal processing for advanced Driver Assistance Systems in Autonomous Vehicles

Signal Processing for Advanced Driver Assistance Systems in ...

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IEEE UP Section conference on Electrical Computer and Electronics (UPCON)

作者： N M Deepika B Rajalakshmi Ginni Nijhawan Ajay Rana Dinesh Kumar Yadav Kadhim A. Jabbar Institute of Aeronautical Engineering Hyderabad India Department of Computer Science New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Amity University Greater Noida Lloyd Institute of Engineering & Technology Greater Noida India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

advanced Driver Assistance Systems (ADAS) in autonomous vehicles are pivotal in ensuring safety, efficiency, and comfort. The core of these systems lies in the sophisticated signal processing algorithms that interpret sensor data to facilitate real-time decision-making. This paper presents a comprehensive study on the application of cutting-edge signal processing techniques for ADAS in autonomous vehicles. A novel framework is introduced, integrating multi-modal sensor fusion, which synergizes data from radar, LIDAR, cameras, and ultrasonic sensors to achieve a robust perception of the vehicular environment. The proposed system employs adaptive filtering to mitigate noise and interference, enhancing the reliability of sensor data under varying conditions. Furthermore, machine learning-based signal processing methods are explored for dynamic object tracking and predictive analytics, enabling proactive maneuvering and risk assessment. The effectiveness of deep neural networks in semantic segmentation and the extraction of critical features from high-dimensional data is also examined, contributing to the precision of object classification and scene understanding. The paper also delves into the challenges of computational complexity and proposes optimization techniques to ensure the real-time performance of ADAS. The results demonstrate significant improvements in detection accuracy, latency reduction, and overall system resilience, marking a substantial advancement in the field of autonomous vehicular technology.

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YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International conference on

作者： M Ranjith Kumar G Jeevan Sendur S Praveen Kumar R Annamalai School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 %

关键词： Computer vision Accuracy Transfer learning Transportation signal processing Real-time systems Character recognition Security License plate recognition Testing

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