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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Saranya, M.D. Kavin, P. Logesh, M. Naveen Kutralanathan, V. Praveen Kumar, S. KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350367171

This research study introduces an innovative approach to generate electrical energy from unconventional sources, specifically from the kinetic energy produced by footsteps, thus reducing reliance on traditional fuel sources. The proposed design incorporates elements like a rack and pinion arrangement, alternator, and chain drive mechanism to convert mechanical energy from walking into usable electricity. The primary goal of this research study is to address the global energy crisis by efficiently converting the kinetic energy from human movement into valuable electrical power. While a single power-generating floor may not fully meet all electricity demands, the ability to generate 100 W of power with just 12 steps represents a significant leap in sustainable energy generation. The project's scalability potential is vast;envisioning multiple floors equipped with similar energy-harvesting systems could result in generating 1 megawatt of electricity. Despite initial cost considerations, the long-term benefits and meaningful contributions to overcome energy shortages and promote sustainability are undeniable. © 2024 IEEE.

关键词： Kinetic energy

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Design of a low power high speed flash ADC

Design of a low power high speed flash ADC

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2024 International Conference on Circuit, Systems and communication, ICCSC 2024

作者： Varma, Nampally Saikiran Pavan, Naspuri Deepak, M. Jamal, K. Reddy, Battu Veera Kumar, Manchalla. O.V.P. Gokaraju Rangaraju Institute of Engineering and Technology Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350365306

This paper's primary goal is to offer potential solutions for creating low-power, high-speed flash ADC circuits. Traditionally, these circuits have required a 2 to the power of N: N decoder circuitry to function, which in turn requires more power to manage because as bit count increases, so does circuit complexity. Here, the task is completed by modifying the conventional decoder circuit using two different methods: one uses a walltree-based circuit, and the other uses a mux-based decoder circuitry. These methods are implemented on the cadence virtuoso tool with 90nm technology. © 2024 IEEE.

关键词： Low power electronics

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RIS-Assisted Wireless Channel Characteristic in Coal Mine Tunnel Based on 6G Mobile communication System

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Progress In Electromagnetics Research C 2024年 141卷 13-23页

作者： Wang, Shuqi Zhang, Wei School of Communication and Information Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China

In the context of 6G communication technology, Reconfigurable Intelligent Surfaces (RIS) can effectively reconfigure signal propagation paths through the adjustment of their passive metamaterial reflector units. This capability mitigates the issue of radio wave attenuation in the complex environments of mine tunnels by optimizing signal paths, thereby reducing energy loss and minimizing coverage dead zones. By utilizing RIS-assisted multi-antenna terrestrial mobile communication channels and ray tracing techniques, researchers have established a wireless channel fading model specifically for rectangular coal mine tunnels. The results suggest that under comparable conditions, RIS technology enhances low-frequency signals (e.g., 2.4 GHz) more effectively than high-frequency signals (e.g., 30 GHz). Furthermore, these improvements are more pronounced as the size of the RIS increases. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Coal mines

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An Explainable Hybrid Learning Model for Indian Food Image Classification 15

An Explainable Hybrid Learning Model for Indian Food Image C...

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

作者： Harishankar, C. Anoop, Neha S. Niranjana, K.S. Biju, Adity Venugopal, Vivek Vekkot, Susmitha Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350370249

As the prevalence of diet management applications continues to rise for personal health tracking, the need for accurate food image classification becomes paramount. While various food classification models exist, understanding and categorizing Indian cuisine remain challenging. Although, deep learning models provide state of the art performance, they lack interpretability in terms of identifying the significant features that lead to accurate classification. In this paper, we propose an Explainable AI backed classification model specifically tailored for Indian food images. We explore the efficacy of multiple deep features learned through standard CNN architectures in conjunction with machine learning algorithms for Indian food classification. In this regard we observe that the features learned from InceptionNet and MobileNet provided superior performance of around 89% with SVM as the classifier. Further we leverage Explainable AI frameworks such as SHAP and LIME to provide insights into the model's decision making process. Through SHAP summary plots we provide understanding of the important deep features contributing for enhanced classification and LIME provides heatmaps that highlights the significant patches in the input image which contributed to the decision. © 2024 IEEE.

关键词： Explainable AI InceptionNet Indian Food Classification MobileNet ResNet VGG16

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Implementation of Microstrip Patch Antenna Array for Detection of Brain Tumor 2

Implementation of Microstrip Patch Antenna Array for Detecti...

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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Rajmin, Meherun Nessa Islam, Sheikh Md. Rabiul Khulna University of Engineering and Technology Dept. of Electronics and Communication Engineering Bangladesh

ISBN: (纸本)9798331517984

In this work, a microstrip patch antenna array designed specifically to detect brain tumors at 2.4 GHz, a frequency that is within the ISM frequency, is presented. The antenna array was meticulously designed to address the challenging issues related to brain tumour detection, while adhering to the safety rules outlined by IEEE standards. The antenna array is constructed using a FR-4 substrate, which has a relative permittivity of 4.3 F/m. The substrate has a thickness of 1.6 mm. The efficacy of the proposed microstrip patch antenna array in cancer detection is meticulously evaluated. The efficacy of the antenna array in detecting brain cancer is assessed by implanting a malignant tumour into a simulated model of the human brain. The simulation findings indicate that the reflectance coefficient experienced a shift of 7.34 dB due to the presence of a tumour. Additionally, the resonance frequency experiences a change of 3.2 MHz in the presence of a tumour. The detection exhibited a difference of 643.26% in gain, 7.77% in directivity, 7.77% in e-field, 2.57% in h-field, and 40.21% in SAR. The SAR is computed using a 10g tissue model. These values meet the safety standards set by the IEEE standard, confirming the feasibility of the suggested antenna for detecting brain cancer. © 2024 IEEE.

关键词： Microstrip antennas

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Oral Cancer Detection Using Convolutional Neural Networks 9

Oral Cancer Detection Using Convolutional Neural Networks

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Jagadesh, T. Kamalesh, P. Kishore, A. Lokin, V. Jaiprakash, B. Kpr Institute of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore India

ISBN: (纸本)9798350365092

In developing nation oral cancer makes the significant threat to human life. The existing system uses deep learning technique for detection of oral cancer in medical imaging. The two powerful tools of deep learning techniques are Convolutional Neural Networks and Deep Belief Network [1]. PSOBER- Particle Swarm Optimization and AI-Biruni Earth Radius a hybrid optimization algorithm is used to optimize the CNN and DBN. To overcome the outperformance of the existing method standard dataset of biomedical images are used for demonstration of the promising results. To test and validate the significance and stability one-way ANOVA and Wilcoxon signed-rank is used. For screening oral cancer detection in clinical setting this technique is used. However, the accuracy and to find the early detection of oral cancer and to make the less suffering of the patients the further research is needed. The proposed method of oral cancer detection using deep learning technique includes the enhancement of the images. In this method salt and pepper noise detection is used to find the accurate results using segmentation of the cells for thresholding. Back Propagation Neural Networks (BPNN) is used for the classification of the oral cancer. Tumor partition and features extraction are done in the proposed method. Feeding the loss backward through neural networks layers to fine tune the weights and the BPNN involves the error rate of forward propagation. Back propagation neural networks contain two signals. They are Error signal and Back signal. © 2024 IEEE.

关键词： Medical imaging

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Traffic Sign Recognition for Automated Speed Control Using Deep Learning 2

Traffic Sign Recognition for Automated Speed Control Using D...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and communications Systems, ICMACC 2024

作者： Velpula, Vijaya Kumar Prasad, S.V.S. Vadlamudi, Jyothisri Yechuri, Sivaramakrishna Miriyala, Ganesh Kumar, Manoj MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Malla Reddy Engineering College Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350366570

Today's newest and most popular topic is real-time driverless cars and their applications. To meet the requirements of these systems, complex algorithms must efficiently perform essential tasks. Autonomous driving technology presents one of the biggest challenges in speed control. We present a new method for speed instruction-based motor control that uses Convolutional Neural Networks (CNNs) to predict speed limits. CNNs extract crucial information from traffic sign signatures, and the performance of our system relies on its ability to accurately identify and classify objects in images. The Arduino microcontroller uses the output of the CNN to control the speed of the robot chassis. Experimental results, both in simulations and real-world tests, demonstrate the effectiveness of our approach. The proposed model achieves a high accuracy of 96% to 97% and outperforms existing models with a faster recognition time, maintaining the required speed with minimal error. These findings highlight the potential of CNN-based systems in enhancing autonomous driving technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis and Investigation of a Novel Compact Metasurafce Absorber with Wide Angle Stability for Triple Band Attributes

Analysis and Investigation of a Novel Compact Metasurafce Ab...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Sudhanva Navaneeth, P.B. Prajwal, G.R. Saifulla, H. Puneeth, P. Dwivedi, Ajay Kumar Singh, Vivek Nagarjuna College of Engineering and Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331533311

This manuscript is about the analysis and investigation of a novel configuration of metasurface absorber which consists triple square ring with a vertical conducting strip. The key characteristics of the absorber design are examined and fine-tuned to demonstrate the successful individual adaptation of each of the three absorption frequency points. The simulation and experimental results demonstrate that the proposed meta-absorber shows exceptional absorption rates and remains unaffected by polarization across a wide range of incoming wave angles in three frequency bands, for both transverse electric (TE) and transverse magnetic (TM) wave modes of operation. The simulated results present that the suggested metabsorber exhibits three absorption peaks at frequencies of 3.5 GHz, 5.2 GHz, and 6.5 GHz, with corresponding absorption values of 94%, 91%, and 96% respectively. The simulation of the proposed model is carried out by HFSS electromagnetic solver. © 2024 IEEE.

关键词： Magnetoplasma

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Enhancing Pneumonia Diagnosis: An Ensemble of Deep CNN Architectures for Accurate Chest X-Ray Image Analysis 2nd

Enhancing Pneumonia Diagnosis: An Ensemble of Deep CNN Archi...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Hasan, Md. Rabiul Ullah, Shah Muhammad Azmat Department of Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (纸本)9789819989362

Various organisms, such as bacterial and viral infections, can cause a lung infection known as pneumonia. It is a significant health concern, particularly in developing and underdeveloped countries with high pollution rates, overcrowding, and limited healthcare infrastructure. In order to effectively treat pneumonia and improve survival rates, early detection is essential. The simplest technique for identifying pneumonia is a chest X-ray (CXR) study, but CXR analysis can be subjective and challenging. In this paper, we have developed a method for automatically detecting pneumonia from CXR images by combining transfer learning and an ensemble of three CNN network architectures (InceptionV3, MobileNetV2, and Xception) with the weighted average ensemble method. We evaluated our approach on chest X-ray datasets, achieving a maximum accuracy of 92% and F1-scores of 87% and 92% for normal and pneumonia, respectively. Our proposed method outperforms existing ensemble techniques and other cutting-edge approaches, demonstrating the potential for improving pneumonia diagnosis through deep learning-based approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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Low Grade Glioma Segmentation Using UNET with ResNext50 Backbone 4

Low Grade Glioma Segmentation Using UNET with ResNext50 Back...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Yadlapalli, Priyanka Kotipalli, Venkat Gali, Devi Sri Prasad Kuntolla, Ramu Gokaraju Rangaraju Institute of Engineering and Technology Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350349900

ALow-grade Glioma is a tumor that arises from the brain's supporting cells called glial cells and is classified as grade II and III on the World Health Organization's grading scale. Gliomas are usually benign, but they can develop into tumors that cause severe neurological symptoms and put pressure on surrounding brain tissue. The majority of these tumors are found in adults, but they can also appear in children. Most of these tumors are treated through surgery, radiation therapy or a combination of the two. Patients may also be monitored with close monitoring if they are asymptomatic, but it's important to note that these tumors can develop into higher-grade tumors over time, so patients should be monitored regularly for any changes in their tumor. Segmentation The process of segmentation is the separation of tumor tissue from surrounding healthy tissue in an MRI scan of the brain. © 2024 IEEE.

关键词： Radiotherapy

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