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Medical image Noise Reduction Methods based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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international conference on Smart systems and Wireless Communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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Deep Joint Source-Channel Coding Using FFT-Enabled Convolutional Neural Network for image Transmission 22

Deep Joint Source-Channel Coding Using FFT-Enabled Convoluti...

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22nd IEEE Consumer Communications and Networking conference, CCNC 2025

作者： Mori, Tomoka Hisano, Daisuke Graduate School of Engineering Osaka University Osaka Japan

ISBN: (纸本)9798331508050

Deep joint source-channel coding (DeepJSCC) has attracted attention as a type of semantic communication that shares not only information but also meaning and intent, and it is a type of deep learning that uses an autoencoder instead of conventional source and channel coding. DeepJSCC with convolutional neural network (CNN) layers has been studied specifically for image transmission, where it avoids cliff effects and achieves higher image quality even in the low signal-to-noise power ratio (SNR) range. In addition, DeepJSCC is suit for services related to the internet of things (IoT), and is likely to be widely applicable to monitoring-related applications such as surveillance cameras. For this reason, it may not be possible to provide sufficient GPU resources, especially for the transmitter where the encoder is installed. Therefore, we propose a fast Fourier transform (FFT)-based DeepJSCC that replaces CNN with FFT and element-wise product to reduce the computation time of DeepJSCC. This paper evaluates the impact of changing the layer structure on image quality. © 2025 IEEE.

关键词： image coding

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Developing an IoT-based Surface Electromyography signal Detection Device for Sarcopenia 6th

Developing an IoT-Based Surface Electromyography Signal Dete...

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6th international conference on Biomedical and Health Informatics-ICBHI

作者： Wang, Po-Xiang Jiang, Ching-Fen I Shou Univ Program Smart Healthcare & Bioinformat Kaohsiung Taiwan

ISBN: (纸本)9783031863226;9783031863233

Sarcopenia is characterized by a persistent reduction in skeletal muscle mass and function, which, if undetected and untreated, can lead to serious consequences such as increased fall risk and long-term immobility. Although sarcopenia has gained attention in recent years, there are currently no effective pharmacological treatments. Muscle loss can only be mitigated in the early stages through resistance training and nutritional interventions. However, precise detection methods used in hospitals are often costly and inaccessible to the general population. Traditional screening methods, like the Sarcopenia Self-Screening Questionnaire (SARC-F), proposed by the Asian Working Group for Sarcopenia, rely on subjective patient reports and lack objectivity. This study aims to address this gap by developing a rapid and convenient sarcopenia screening device that can be used at home or in community settings. Leveraging internet of Things (IoT) technology, we developed a surface electromyography (sEMG) system to measure the muscle activity of the gastrocnemius, tibialis anterior, and quadriceps during walking and standing. The muscle signals were wirelessly transmitted using the MQTT protocol for backend analysis, enabling real-time gait and muscle strength monitoring. Twenty-eight healthy elderly individuals from the community participated in this study. To validate the effectiveness of the device, traditional sarcopenia-related physical measurements were obtained using standard methods. We first classified participants into four sarcopenia risk levels using the above data. In the meantime, we used deep image gait analysis software (GaitBEST) to classify it into four risk levels. Preliminary results show that these two classification levels had a slight discrepancy. However, the sEMG signals obtained using the developed device reflect the risk level determined by gait pattern, indicating the necessity of further analysis to explore the relation between gait and skeletal muscl

关键词： Sarcopenia Deep image Analysis IoT Surface Electromyography (sEMG)

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Channel Adaptive Decoding in Underwater Optical Camera Communication systems 5

Channel Adaptive Decoding in Underwater Optical Camera Commu...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Aberathna, Thimuthu Kumarage, Miyuni Atthanayake, Nuwan Sri Lanka Technology Campus Faculty of Engineering Padukka Sri Lanka

ISBN: (纸本)9798331530983

Underwater optical camera communication (UOCC) systems that use light-emitting diodes as transmitters and high-frame rate cameras as receivers offer robust and low-cost wireless communication solutions for underwater applications compared to traditional methods. However, its performance is greatly affected by the varying underwater environments such as turbulence, salinity, and oceanic currents. This is mainly due to fixed image processing and data decoding algorithms used in UOCC systems don't provide adaptive methods that change with the underwater environments. To this end, we introduce an adaptive threshold-based algorithm to enhance the system's performance by dynamically adjusting the threshold values used in the decoding algorithm on real-time channel *** this context, adaptive thresholding refers to selecting optimal threshold values for various transmission scenarios, ensuring robust data decoding tailored to specific environmental challenges. This paper presents a comprehensive analysis of the presented algorithm under four distinct channel conditions, such as clear water, muddy water, air bubbles, and chlorophyll water. Our simulations and experiments demonstrate the effectiveness of the adaptive threshold-based algorithm in improving bit error rate (BER), making UOCC more reliable and robust in diverse underwater conditions for short-range applications. The BER performance of the presented algorithm in clear water is nearly zero, while under the other three conditions, it is approximately 1 × 10-3 within a 1-meter communication distance. © 2025 IEEE.

关键词： signal systems

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Tunable Q-factor Wavelet Transform based Features for Classification of Emotions Using EEG signals 4

Tunable Q-factor Wavelet Transform Based Features for Classi...

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4th international conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Das, Kaushik Saha, Moumita Dept. of Electronics and Communication Engineering National Institute of Technology Jamshedpur Jharkhand India School of Electronics Engineering Vellore Institute of Technology Andhra Pradesh India

ISBN: (纸本)9798331530402

Understanding human emotions is crucial for effective communication and interaction. Emotion recognition systems have gained considerable attention due to their potential applications in various fields, such as healthcare, human-computer interaction, and entertainment. Electroencephalography (EEG) has emerged as a promising modality for emotion recognition, offering insights into the underlying neural processes associated with different emotional states. This study investigates the effectiveness of the tunable Q-factor wavelet transform (TQWT) for classifying different emotions using EEG signals. TQWT decomposes the EEG signal into subbands, and some features such as variance, skewness, kurtosis, and sample entropy are calculated from these subbands. These features serve as input to the support vector machine (SVM) classifier to classify emotions like happiness, fear, sadness, neutral, and relaxation. Experimental results of this proposed method demonstrate superior performance in classifying these five emotions compared to other existing methods. © 2025 IEEE.

关键词： image reconstruction

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Automated Traffic signal System Incorporating Real-Time Traffic

Automated Traffic Signal System Incorporating Real-Time Traf...

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international conference on Innovative Methods and Practical Applications for Cognizant Transportation systems, IMPACTS 2023

作者： Saseendran, Athulya Worin, Jerin Jyothika, K.J. Ravindran, Ragi C. Sahrdaya College of Engineering and Technology Kodakara India

ISBN: (纸本)9789819772995

Traffic management is a major challenge in cities where the roads are congested with a greater number of vehicles than their capacity. This congestion leads to delays, affects the productivity and also, contributes to air pollution. Current traffic control systems are static and ineffective in handling the complex traffic situations. Hence, the need of an automatic traffic control system that can effectively control and manage the real time traffic. The proposed system uses live images from traffic cameras to detect the vehicle type and calculate the traffic density with image Processing and Artificial Intelligence. A specific model is taught to identify the various kinds of vehicles, such as cars, motorbikes, auto rickshaws, and larger vehicles, and the You Only Look Once (YOLO) method is utilized to detect the vehicles. Also, this system changes the traffic lights dependent on the current traffic density to lessen potential clogs, speed up transit, and clean up the air. OpenCV can draw coordinates on images using the object detection output, which is in JSON format. A cloud computing system calculates cycle time and updates signal timing based on traffic density. This approach has the potential to reduce vehicle waiting time, congestion, maintenance costs, and air pollution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning image processing

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Accident Detection and Reporting System for Powered Two-Wheelers Using internet of Things and Edge AI augmented with MCUSUM features

Accident Detection and Reporting System for Powered Two-Whee...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Viswanath, Vijay Unnikrishnan, S. Saeed, S. Dhanya, V.S. Deepa, P.P. Selvan, Kalai Emerging Technologies Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798331507541

According to data provided by the Ministry of Road Transport and Highways (MORTH), Government of India, about 1.68 lakh people have lost their lives in accidents and nearly 4.4 lakh people have suffered injuries [1]. The number of accidents has increased by approximately 12% year on year. The data also show that people affected by accidents are likely to be in the age group of 15 to 49 years. This age group is virtually the pillar that supports the nation's development. Therefore, road fatalities can indirectly influence the nations development. In this work, we have implemented a system aimed towards powered two-wheelers (PTWs) that detects accidents and sends an SOS signal to a preconfigured contact. The system, upon detecting an accident/critical event, can attempt to convey the SOS signal in multiple modalities for redundancy. The SOS signal includes the last known GNSS coordinates of the vehicle to be able to pinpoint the location of the incident. The system also has a manual SOS trigger option that can be used as a panic button. Detection is performed by a Convolutional Neural Network (CNN) based classifier running on the system using Edge AI capability that uses sensory output from an inertial measurement unit (IMU) and its derivative data for input features. In this work, we have experimented with the possibility of improving the classification performance of the classifier by augmenting the input feature set with Multivariate Cumulative Summation (MCUSUM) [2] control chart statistic of the IMU data. The results show that this technique can give marked improvements in the overall accuracy of the classification system. © 2025 IEEE.

关键词： Emergency services

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RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D Object Detection in Autonomous Driving

RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D ...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Cao, Xingjian Wang, Ping Zhang, Zhitao Tu, Huizhao Chen, Yong Liang, Zhenbao College of Electronic and Information Engineering Tongji University Shanghai China College of Transportation Tongji University Shanghai China Co. Ltd. Ningbo China

ISBN: (纸本)9798350368741

Accurate and reliable 3D object detection is crucial for autonomous driving, normally achieved using camera-only or camera-LiDAR fusion methods based on BEV (Bird's Eye View) perspective. However, visual perception through cameras alone faces significant challenges, such as ambiguous depth estimation and poor performance in low-light, while camera-LiDAR fusion, though robust, are expensive on deployment and have limitations in dust and fog weather conditions. To address these issues, we propose RAFDet (Radar-Assisted Fusion Detection), a novel multi-modality fusion framework integrating camera and radar data for enhanced 3D detection. RAFDet utilizes radar RF (Radio Frequency) images to enrich spatial details, boosting fusion effects, while radar point clouds provide precise depth information to rectify visual depth predictions. In addition, the Edge-Awareness Feature Enhancement mechanism compensates for the sparsity of radar points, further refining depth estimation and detection accuracy. Our extensive experiments demonstrate the superiority of RAFDet over current methods, highlighting its potential for autonomous driving systems. Key results show significant improvements in depth prediction and overall performance metrics, validating the effectiveness of our approach. Our data collection and testing in real traffic scenarios also reflects the robustness, accuracy, and cost-effectiveness of RAFDet for 3D object detection in autonomous driving. © 2025 IEEE.

关键词： 3D object detection Autonomous driving Camera-radar fusion Depth-rectification view transformation Edge-awareness feature enhancement Radar RF image

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Improving Digital Connectivity Using Real-Time signal Mapping and Adaptive Network Planning for IoT and 5G Networks

Improving Digital Connectivity Using Real-Time Signal Mappin...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： Dileep Reddy Bolla Apoorva Aashritha Suresh Poojary Ramesh Naidu Computer Science and Engineering Nitte Meenakshi Institute of technology Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The goal of this project is to increase digital connection in expansive settings, such college campuses, by utilizing 5G IoT Repeaters for adaptive network planning and real-time signal mapping. Weak signal areas in a grid-based model are identified using a MATLAB-based simulation framework, and additional IoT devices (5G Repeaters) are positioned strategically to improve network coverage. signal strength and coverage indicators are enhanced through an iterative process, and the results are displayed with bar plots and heatmaps to provide actionable insights. The results show notable gains in signal quality, latency reduction, and network dependability, underscoring the potential of 5G IoT Repeaters for high-performance, scalable connection. This method supports applications in education, IoT-driven campus management, and smart city efforts by providing an economical and effective way to fix coverage gaps in dynamic contexts. The Research work is effective in identifying Realtime Heatmap of signal Strength which shows the distribution of weak and strong signal zones and the IoT device and 5G Devices

关键词： Heating systems Adaptive systems 5G mobile communication Repeaters Real-time systems Planning internet of Things Object recognition MATLAB signal mapping

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