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检索条件"机构=Dept. of Computer and Electrical Engineering and Computer Science"

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User Authentication and Vital Signs Extraction from Low-Frame-Rate and Monochrome No-Contact Fingerprint Captures 58

User Authentication and Vital Signs Extraction from Low-Fram...

引用

58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Olugbenle, Olaoluwayimika Drake, Logan Venkataswamy, Naveenkumar G. Rahman, Arfina Afolayanka, Yemi Imtiaz, Masudul Banavar, Mahesh K. Clarkson University Dept. of Electrical and Computer Engineering PotsdamNY United States

ISBN: (纸本)9798350354058

We present our work on leveraging low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users. These videos utilize photoplethysmography (PPG), commonly used to measure vital signs like heart rate. While prior research predominantly utilizes high-frame-rate, multi-wavelength PPG sensors (e.g., infrared, red, or RGB), our preliminary findings demonstrate that both user identification and vital sign extraction are achievable with the low-frame-rate data we collected. Preliminary results are promising, with low error rates for both heart rate estimation and user authentication. These results indicate promise for effective biometric systems. We anticipate further optimization will enhance accuracy and advance healthcare and security. © 2024 IEEE.

关键词： Photoplethysmography

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A Formal Quality Control System for Millimetre-Wave Channel Sounder Verification 100

A Formal Quality Control System for Millimetre-Wave Channel ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Chen, Xin Michelson, David G. The University of British Columbia Dept. of Electrical & Computer Engineering VancouverBC Canada

ISBN: (纸本)9798331517786

Various hardware and software defects may distort or impair the channel-measurement data produced by millimetre-wave channel sounders. The IEEE Standard Association's P2982 standards development effort was begun with the understanding that the ad hoc and incomplete verification methods in common use today are inadequate for the task and a consensus-based approach is the best method to achieve methods that are complete, effective, and economical and which will be widely accepted and adopted. Past efforts have focused on identifying discrepancies between actual and expected channel responses, but have not incorporated these processes into a formal quality control system. Here, we show how formal quality control can be incorporated into the P2982 vison for millimetre-wave channel sounder verification and thereby: 1) allow more effective identification and correction of shortcomings in channel sounder performance and/or post-processing techniques, 2) give greater confidence that a given set of channel measurement data is suitable for inclusion in a pooled database, and 3) align more closely with metrology practices in other disciplines. © 2024 IEEE.

关键词： Sounding apparatus

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Physical-Design Aware Scan Cell Reordering for Low Power Testing

Physical-Design Aware Scan Cell Reordering for Low Power Tes...

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2024 IEEE East-West Design and Test Symposium, EWDTS 2024

作者： Badjatya, Rohit Vinay Kumar, Rahul Tudu, Jaynarayan T Fujita, Masahiro Singh, Virendra Indian Institute of Technology Bombay Dept. of Electrical Engineering Mumbai India Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Tirupati India University of Tokyo Dept. of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798331515768

Scan chain architecture is widely employed in modern VLSI design for test applications. However, it often leads to high power consumption during testing. The architecture experiences elevated simultaneous switching activity due to non-functional input patterns applied during test time, resulting in significant peak power in the circuits. This increased power consumption can cause IR drop issues, negatively impacting the chip's yield. This paper proposes an innovative approach to reduce both peak power and routing length by effectively reordering the scan chains. Experimental results show that the proposed approach is effective for the ISCAS'89 and ITC'99 benchmark circuits and this method achieves lower peak power and routing length compared to the state of the art. © 2024 IEEE.

关键词： low power testing peak power routing constraint scan chain reordering voltage drop

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A Novel Method to Detect Oral Carcinoma Using Box Annotation Based on YOLO Model

A Novel Method to Detect Oral Carcinoma Using Box Annotation...

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2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Laila, Shahara Ema, Romana Rahman Galib, Syed Md. Jamil, Abu Rafe Md Uddin, A. F. M. Shahab Jashore University of Science and Technology Dept. of Computer Science and Engineering Jashore Bangladesh

ISBN: (纸本)9798350350289

Oral carcinoma affects millions of individuals globally and is a major public health problem due to its high fatality rate. Treatment effectiveness depends on early detection and improves survival rates. An effective method for automated oral cancer screening is deep learning. The paper's primary goal is to use deep learning techniques to advance the state of oral cancer diagnosis, specifically YOLOv5 and YOLOv7. We aimed to achieve accurate detection by leveraging bounding box annotations for both two distinct types of data: collected clinical data and benchmark data of oral carcinoma. The proposed methodology encompasses data collection, preprocessing, expert-driven bounding box annotation and the use of cutting-edge deep learning models for lesion identification, YOLOv5 and YOLOv7. Bounding box serving as expert annotations are meticulously added to pinpoint cancerous regions within the images. Subsequently, a comprehensive evaluation is conducted, measuring the performance of both models based on precision, recall rate (sensitivity) and mean average precision (mAP). Our findings reveal that YOLOv5 outperforms YOLOv7 in terms of oral cancer detection, demonstrating superior precision and recall rates. In the evaluation of this research, the YOLOv5 model demonstrated strong performance metrics on both clinical and lip and tongue cancer datasets. For the lip and tongue cancer dataset, the YOLOv5 model achieved a precision of 97.2%, a mAP (mean Average Precision) of 97.7% and a recall of 96.3%. © 2024 IEEE.

关键词： Clinical research

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Augmenting Underwater Plastic Detection: A Study with YOLO-V8m on Enhanced Datasets 3

Augmenting Underwater Plastic Detection: A Study with YOLO-V...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Reddy, Veeramreddy Bharath Kumar Basha, Shaik Sameer Swarnalakshmi, J. Hindustan Institute of Technology and Science Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Ocean animals and plants are in danger because there is a lot of trash in the water. This old way of keeping an eye on trash made of plastic in the ocean is hard to do, takes a long time, and can't be used to check the whole ocean. In this plan, the new deep learning algorithms, called You Only Look Once version 8 (YOLOv8), are used to find and sort plastics in the ocean right away. There's a new way to handle this subject. Everyone knows YOLOv8 for how fast and well it can find things. It has been changed in this study to deal with the problems that only happen when taking pictures underwater, like changing light, cloudy water, and different kinds of plastic. A unique set of labeled underwater pictures are used to help it find different kinds of plastic very accurately and quickly. They are different sizes and shapes. Together, YOLOv8 and underwater robotic platforms make a useful and adaptable way to always keep an eye on the ocean. This collects useful information that can be used to protect the environment and lower pollution. Better tools for keeping an eye on the environment are made possible by the study. It also shows how cutting edge AI can be used to solve big environmental problems. © 2024 IEEE.

关键词： Object detection

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Optical Mark Recognition with Object Detection and Clustering 6

Optical Mark Recognition with Object Detection and Clusterin...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tabassum, Kaifa Rahman, Zhalok Shahjalal University of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. They cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. These results demonstrate the efficacy of our approach to OMR sheet analysis. © 2024 IEEE.

关键词： Object detection

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Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models 2

Advanced Forecasting and Analysis Techniques for Optimizing ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raikar, Gayathri Harshith, D. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bangalore India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment. © 2024 IEEE.

关键词： Channel Performance Data Analysis Time Series Forecasting User Engagement Website Performance

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Wearable Device With Speech and Voice Recognition for Hearing-Impaired People 21

Wearable Device With Speech and Voice Recognition for Hearin...

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21st International Learning and Technology Conference, L and T 2024

作者： Tharwat, Marwa Wardak, Yasmin Balbaid, Shumokh Radin, Ejlal King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Hearing impairment is a widespread health condition that affects millions of people worldwide. It refers to the lack of or low functionality of the hearing organs, resulting in difficulties in effective communication and participation in everyday conversations. This limitation can negatively impact various aspects of the social and personal lives of the hearing-impaired individuals. Moreover, the high cost and limited availability of assistive devices further restrict accessibility for those with limited financial resources. To address these challenges, this study develops an affordable wearable device equipped with speech and voice recognition technologies to enhance the accessibility of spoken language for individuals with hearing impairment. OpenAI Whisper is employed for real-time conversion of spoken words into written text. Voice recognition, utilizing Mel Frequency Cepstral Coefficients (MFCCs) and Random Forest (RF) classifier, identifies and distinguishes speakers within the user's family and friends zone. The recognized spoken words from the speech recognition process and the speaker's identity from the voice recognition process are combined and displayed on a screen to the hearing impaired. By combining speech and voice recognition technologies, the proposed device breaks communication barriers for individuals with hearing impairment and enhance their overall communication experience. © 2024 IEEE.

关键词： Speech recognition

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Interference Mitigation and Resource Allocation in Integrated TN-NTN Systems via Deep Q-Learning 15

Interference Mitigation and Resource Allocation in Integrate...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Park, Seoyeong Jung, Soyi Ajou University Dept. Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

As the demand for spectrum increases, the coexistence of terrestrial networks (TNs) and non-terrestrial networks (NTNs) is becoming a critical aspect of 6th generation (6G) communication scenarios. Integrated TN-NTN systems share the same frequency bands, leading to significant performance degradation due to co-channel interference. This paper proposes a deep Q-network (DQN) based deep reinforcement learning (DRL) algorithm to efficiently allocate resource blocks within shared frequency bands to each user equipment (UE) in integrated TN-NTN systems. The proposed algorithm continuously interacts with the network environment to learn optimal resource allocation policies. The proposed algorithm continuously interacts with the network environment to learn optimal resource allocation policies, thereby minimizing interference and maximizing system data throughput, promoting efficient use of spectrum resources. © 2024 IEEE.

关键词： Deep reinforcement learning

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Effective Polyp Segmentation in Medical Imaging: A Semi-Supervised Approach with Progressive Supervision 3

Effective Polyp Segmentation in Medical Imaging: A Semi-Supe...

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3rd International Conference on Big Data, Information and computer Network, BDICN 2024

作者： Ye, Shunlong Ji, Zexuan Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350363821

Medical imaging plays a pivotal role in diagnosing and monitoring various diseases, and accurate polyp segmentation is essential for early disease detection and treatment planning. However, the scarcity of well-Annotated medical imaging data hampers the development of effective segmentation models. Semi-supervised learning stands out as a critical approach to address this issue. Traditional semi-supervised medical image segmentation models leverage strong-weak perturbation consistency losses to provide supervision signals. While these losses are effective in promoting consistency, they often exhibit a drawback, that is the dominance of strong perturbations during early training stages. This dominance hinders the model's ability to extract meaningful features, ultimately impacting its overall performance. In response to these challenges, we introduce a novel training method applied to polyp segmentation. We employ a progressive approach, gradually introducing strong-weak perturbation supervision signals. This gradual guidance empowers the model to focus on learning relevant features from the beginning of training, mitigating the issue of early dominance and enhancing its performance. Our method is evaluated on two widely-used polyp segmentation datasets and surpasses state-of-The-Art methods, demonstrating substantial improvements in segmentation accuracy. © 2024 IEEE.

关键词： Self-supervised learning

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