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检索条件"机构=Computer Applications in Science and Engineering dept."

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Performance Evaluation of Triple-Branch Diversity Receivers in Composite Gamma-Shadowed Rician Fading Channels with AMC and Asymmetric SNR Conditions

Performance Evaluation of Triple-Branch Diversity Receivers ...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Devansh Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

This paper provides an in-dept. analysis of the performance enhancement resulting from AMC in triple-branch diversity receivers under composite Gamma shadowed Rician fading channels with uneven SNR conditions. This paper was able to show the level of enhancement in average SNR resulting through simulations of AMC on signal quality in several receiver branches, which clearly shows AMC's ability in alleviating fading and shadowing based degradations. The analysis revealed that AMC adequately alleviates degradations by changing modulation schemes and coding rates based on the current quality of the channel. This reinforces AMC's ability to improve the link's reliability and likelihood to optimize the efficiency of the communication channel. This makes AMC a desirable design and optimization feature for wireless communication in highly fading and shadowed environments, which makes this innovation a potential fundamental technology in the realization of robust, resilient, and adaptive communication systems.

关键词： Wireless communication Fading channels Degradation Technological innovation Adaptive systems Rician channels Modulation Receivers Encoding Signal to noise ratio

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Beyond the Blink: IoT's Next-Gen Drowsiness Detection System for Drivers

Beyond the Blink: IoT's Next-Gen Drowsiness Detection System...

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Industrial Electronics: Developments & applications (ICIDeA), IEEE International Conference on-

作者： Sidheswar Routray Nachiket Patel Dhruv Patel Jyoti Srivastava Rudra Narayan Dash Dept. of Computer Science & Engg. Pandit Deendayal Energy University Gandhinagar India Dept. of Computer Science & Engg. Indrashil University Mehsana India Dept. of Computer Engineering Ganpat University Mehsana India School of Electrical Engineering KIIT University Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Drowsiness during driving includes high risks for the driver, the co-passengers, and the other people on the road. Advance Driver Assistance Systems (ADAS) have been proposed to reduce these risks. This work introduces a new Internet of Things (IoT) seizure detection system to improve driver safety using Raspberry Pi, which is equipped with various sensors and utilizes machine learning for real-time monitoring and analysis. Using sensor fusion technology, including monitoring yawning and eye-closing patterns, the system aims to detect early signs of driver sleepiness. Being a low-cost, versatile, and tiny device, it becomes an ideal core processor for integration into an in-car environment. Additionally, the simplicity enables seamless communication with various sensors and facilitates effective data collection. Extensive tests and simulations were conducted to evaluate the effectiveness and reliability of the proposed system for real-world driving situations. The result outcomes prove the system's accuracy and efficiency in detecting early signs of driver fatigue to enhance driver safety. The integration of Raspberry Pi and sensor fusion technology makes it economical as well as scalable approaches supporting multiple platforms.

关键词： Sleep Roads Sensor fusion Sensor systems Real-time systems Sensors Safety Internet of Things Reliability Vehicles

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Security Augmentation Approach for Internet of Things Enhanced Cryptographic Algorithm 6

Security Augmentation Approach for Internet of Things Enhanc...

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Kumbalavati, S.B. Bendigeri, Kirankumar Y. Mallapur, Jayashree D. Pareek, Piyushkumar Patil, Mahesh S. Dept. of Electronics & Instrumentation Eng. Karnataka Bagalkot India East West College of Engineering Dept. of Computer Science Eng. Karnataka Bangalore India KLE Technological University Dept. of Computer Science Eng. Karnataka Hubli India

ISBN: (纸本)9781665410359

The Internet of Things (IoT) is continuously achieving pragmatic attention for research and investigation as this technology have achieved considerable popularity worldwide because of rapidly connecting billions of apparatus to internet and capable enough to make a pragmatic connection among diverse devices for data communication as desired in modern world. Several investigations have been carried out on IoT secrecy in recent years for protecting the confidential data as well as information but due to rapid technological advancements in modern devices as well as communication systems along with new technologies, the protection of the confidential data or information is becoming a critical threat all over globe. In this paper, authors suggested a novel security augmentation approach for IoT utilizing an enhanced cryptographic algorithm that is capable enough to secure confidential data as well as information in faster and pragmatic manner in comparison to the conventional approaches and establish pragmatic connectivity in smart city model as demanded in present era. This experimentation work was carried out over a personal computer (PC) installed with the MATLAB R2019b software kit, 16 GB of RAM (Random Access Memory), Window 10 along with the 64-bit operating system (OS). The measured results were observed with high degree of accuracy and precision for pragmatic validation. The measured outcomes are pragmatic and improved in comparison to diverse kinds of conventional strategies. Although, multifarious investigators have suggested diverse strategies in previous years to improve secrecy of confidentiality data but there are still gigantic possibilities in this arena for more investigation. © 2022 IEEE.

关键词： Internet of things

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Autism Spectrum Disorder Classification via Local and Global Feature Representation of Facial Image

Autism Spectrum Disorder Classification via Local and Global...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Mahamood, Md. Nadim Uddin, Md. Zasim Shahriar, Md. Arif Alnajjar, Fady Rahman Ahad, Md Atiqur Begum Rokeya University Department of Computer Science and Engineering Rangpur Bangladesh College of Information Technology United Arab Emirates University United Arab Emirates University of East London Dept. of Computer Science and Digital Technologies United Kingdom

ISBN: (纸本)9798350337020

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction. Early diagnosis of ASD can mitigate the severity and help with ideal treatment direction. computer vision-based methods with traditional machine learning and deep learning are employed in the literature for automatic diagnosis. Recently, deep learning with a facial image-based ASD classification has gained interest due to its ease of collection and non-invasiveness. We observed that the existing approaches utilized either local or global features of facial images to diagnose ASD. However, its important to consider both local and global features to obtain fine-grained details and larger contextual information for accurate detection and classification. This paper proposes a sequencer-based patch-wise Local Feature Extractor along with a Global Feature Extractor. Finally, the features from these modules are aggregated to obtain the final feature for the classification of ASD. Experiments on a publicly available Autism Facial Image Dataset demonstrate that our proposed framework achieves state-of-the-art performance. We achieved accuracy, precision, recall, and F1-score of 94.7%, 94.0%, 95.3%, and 94.6%, respectively. © 2023 IEEE.

关键词： and Global feature extraction ASD Autism Spectrum Disorder classification Local feature extraction LSTM Vision-transformer

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High-Performance Face Identification using Hyperspectral Imaging to Counteract Deep Fake Biometrics

High-Performance Face Identification using Hyperspectral Ima...

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IEEE International Conference on Robotic Computing (IRC)

作者： Sota Furusawa Chinthaka Premachandra Dept. of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

This research addresses the security challenges posed by Generative Adversarial Networks (GANs) in biometric authentication, focusing particularly on the use of Hyperspectral Imaging (HSI) to counteract the threat of deep fake biometrics created by GANs. We sought solutions to the issues identified in a previous study, such as the inability to handle images of individuals not previously trained and the high time consumption required for identification. In this paper, we first reduced the dimensionality of hyperspectral face data used for training, and then conducted a binary classification of “target subject (class)” and “nontarget subjects (classes).” A discriminator, based on the Least Squares Generative Adversarial Network (LSGAN) model, was developed using HSI for each class for binary classification. The proposed method significantly reduced identification time and achieved very high accuracy in identification.

关键词： Training Deepfakes Face recognition Biological system modeling Focusing Generative adversarial networks Security Robots Hyperspectral imaging Principal component analysis

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A Wearable RF Energy Harvester Based on Spoof Surface Plasmonic Antenna Array

A Wearable RF Energy Harvester Based on Spoof Surface Plasmo...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Liu, Zhu Li, Jinxin Xiao, Pei Hunan Normal University Dept. Physics and Electronics Changsha China Hunan University Dept. Computer Science and Electronic Engineering Changsha China Hunan University Dept. Electrical and Information Engineering Changsha China

ISBN: (纸本)9781665452366

This paper presents a wearable RF energy harvester based on spoof surface plasmonic (SSP) antenna array. Four separate antennas are connected by the four bending SSP waveguides, and the SSP antenna array is formed. The loop is applied to excite the SSP structure to generate surface waves and converted to the radiative wave into the space. The rectify circuit is integrated with loop to output DC power and fabricated by the polyimide so that it can conform with different sensing modules. The proposed harvester is fabricated by the conductive and nylon fabrics in a compact and low-profile structure to be easily integrated with clothing. The maximum harvested RF-DC power is 126.24 μ W under a power density of 2.75 μ W/cm2 when the proposed structure is worn on phantom. The simulation and measurement results demonstrate that the wearable RF energy harvester has the potential to power body sensor network. © 2022 IEEE.

关键词： Antenna arrays

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Enhancing Emotional Support Chatbots Using Deep Learning & NLP: A Study on Personalization and Effectiveness

Enhancing Emotional Support Chatbots Using Deep Learning & N...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Renuka Sanjay Parihar Md. Saifullah Khalid Umesh Chandra Sharma J. Josphin Mary Dept. of Computer Science Chandigarh University Chandigarh India Dept. of Humanities & S.Science Madhav University Pindwara India Dept. of Bus. Management & Commerce Usha Martin University Jharkhand India Dept. of Computer Eng. & Applications Mangalayatan University Aligarh India Dept. of Computer Science Meenakshi A.H.E.R Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This research focuses on utilizing deep learning and natural language processing (NLP) to design an AI-powered chatbot for emotional support. The primary objective is to establish a supportive platform for individuals dealing with emotional challenges. By leveraging NLP, the chatbot will identify and interpret users’ emotional states, generating tailored and empathetic responses. Deep learning techniques will enable the system to learn from past interactions, adapting its responses to meet the unique needs of each user. The platform aims to deliver personalized, scalable support while incorporating features like sharing uplifting content, such as cute cat images. This study emphasizes the potential of AI-driven solutions in enhancing mental well-being through empathetic and meaningful interactions.

关键词： Deep learning Emotion recognition Accuracy Virtual assistants Telemedicine Customer services Mental health Chatbots Artificial intelligence

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Identification of Genomic Associations Between Parkinson's and Neurodegenerative Diseases Using Bioinformatics Models

Identification of Genomic Associations Between Parkinson's a...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Shohel Rana Nitun Kumar Podder Humayan Kabir Rana Md. Imam Hasan Md. Shafiul Azam Md Abdur Rahim S. M. Hasan Sazzad Iqbal Subir Saha Dept. of Computer Science and Engineering Pabna University of Science and Technology Pabna Bangladesh Dept. of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

Neurodegenerative diseases (NDGs) can slowly damage the brain as well as the central and peripheral nervous systems. Parkinson's disease (PD) is a sensitive sensory disease that causes a malfunction of dynamic equilibrium. PD causes certain brain neuronal cells to gradually split or die. There are certain significant characteristics of PD that may increase the risk of acquiring deferent NDGs including Alzheimer's disease (AD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Multiple sclerosis disease (MSL). We conducted a transcriptomic analysis to find connections between PD and NDGs. To explore how PD may affect the advancement of NDGs, we employed a quantitative framework to analyze the gene expression microarray dataset. To obtain genomic associations between PD and NDGs, we performed gene expression profiling by identifying dysregulated pathways, gene ontologies, PPIs, and phylogenetic analysis. We found 15, 11, 28, and 24 significant genes are shared between PD and AD, ALS, HD, and MSL respectively. We also identified a significant number of functional and ontological pathways, hub proteins and phylogenetic associations that indicate PD may have a link to develop NDGs (AD, ALS, HD, and MSL). We validate our findings through gold benchmark databases. This systematic approach is highly beneficial in understanding the causes and progression of NDGs owing to the effect of PD.

关键词： Proteins Peripheral nervous system Multiple sclerosis Systematics Parkinson's disease Genomics Phylogeny

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Design Optimization and Performance Analysis of Rectangular Structured Moving Magnet Linear Actuator 4

Design Optimization and Performance Analysis of Rectangular ...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ahmad, Hussain Jawad, Muhammad Ahmad, Zahoor Ahmad, Aftab Sarhad University of Science & Information Technology Dept. Electrical Engineering Peshawar Pakistan School of Electrical Engineering Southeast University Nanjing China Comsats University Islamabad Abbottabad Campus Dept. of Electrical and Computer Engineering Abbottabad Pakistan School of Electrical Engineering Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9798350335316

This article examines a new linear oscillating actuator (LOA) design that uses rectangular-shaped core materials and permanent magnets (PMs). The paper's primary objective is to examine a novel LOA topology with a rectangular-shaped core and PMs as an alternative to the tubular LOA. A Static core material is housed between the mover's components, and two stator cores with two coils each are placed on either side of the mover. The dimensions of all the parameters are swept for optimization, and the optimal parameter dimension is chosen based on the optimal value of the electromagnetic (EM) force. An analysis is done for output parameters like EM force and stroke. EM force per PM mass of the investigated design of LOA is 67 percent higher than of conventional rectangular-shaped LOA. Additionally, the proposed design's EM force density is 23.8 percent higher than that of the conventional design of LOA. Furthermore, the stroke of the proposed LOA is also feasible and more than most of the designs of the LOA. Additionally, the proposed design of the LOA is simple structure, low-cost, and feasible for fabrication. © 2023 IEEE.

关键词： Topology

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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