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检索条件"机构=Electronics and Computer Engineering Technology"

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Efficient Design of a Single-Balanced Diode Mixer for 5G Communication Systems 5

Efficient Design of a Single-Balanced Diode Mixer for 5G Com...

引用

5th International Conference on Computing, electronics and Communications engineering. iCCECE 2022

作者： Sellidj, Abdelmounim Gaoua, Said Yagoub, Mustapha C.E. University of Science and Technology H. Boumedienne Faculty of Electronics and Computer Science Algiers Algeria University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

This work discusses the design of a broadband single-balanced down converter mixer, operating in the 5G NR FR2 bands of 24.25-29.5 GHz. With an IF frequency of 250 MHz, the mixer presents excellent port-To-port isolation over almost all the targeted band, i.e., 94.6 dB, 72.6 dB, and 25 dB for LO-IF, RF-IF, and RF-LO ports, respectively. It also exhibits low conversion loss (7.49 dB) and noise figure (with a maximum of 6.38 dB and 6.29 dB for double-and single-sideband, respectively). Furthermore, it displays a good linearity with a 1-dB compression point of 10.5 dB as well as input/output third order intermodulation products (IIP3/OIP3) of 18.68 dBm and 11.03 dBm, respectively. Such performance made the proposed design a suitable candidate for 5G applications. © 2022 IEEE.

关键词： Mixers (machinery)

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A Comprehensive Analysis on Co-Saliency Detection on Learning Approaches 3

A Comprehensive Analysis on Co-Saliency Detection on Learnin...

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3rd International Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Agrawal, Anrag Vijay Kumari, Tadi Satya Soniya, M. Meenakshi, S. Jegadeesan, R. Ahmad, S. Rehan Ict Academy Indian Institute of Technology Department of Electronics and Communication Engineering Uttarakhand India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India Tamil Nadu India Jyothishmathi Institute of Technology & Science Department of Computer Science and Engineering Telangana India Hiralal Mazumdar Memorial College for Women Department of Zoology Kolkata India

ISBN: (纸本)9798350336238

Co-saliency recognition is a fast-growing field in computer vision. Co-saliency detection, a unique field of optical sensitivity, relates to the identification of shared and salient backgrounds from two or even more pertinent images, and it is broadly applicable to a variety of computer vision tasks. Various co-saliency detection techniques are composed of three modules: extracting image characteristics, examining informative signals or elements, and creating effective computer foundations to construct co-saliency. Even though several strategies have been created, there hasn't yet been a thorough analysis and assessment of co-saliency prediction methods in the literature. This work intends to provide a thorough analysis of the foundations, difficulties, and implications of co-saliency detecting. An overview is offered based on the connected computer vision works, investigates the detection history, outline and classify the important algorithms and address certain unresolved difficulties, explain the possible co-saliency identification applications as point out certain unsolved obstacles and interesting future appears to work. © 2023 IEEE.

关键词： computer vision

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An Efficient Application of Machine Learning for Assessment of Terrain 3D Information Using Drone Data

An Efficient Application of Machine Learning for Assessment ...

引用

Virtual International Conference on Soft Computing, Optimization Theory and Applications, SCOTA 2021

作者： Agarwal, Ankush Saini, Aradhya Kumar, Sandeep Singh, Dharmendra Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India

ISBN: (纸本)9789811964053

Plant height is beneficial in defence-related applications during the movement of troops as terrain information is required in advance. This terrain information is of utmost importance to obtain knowledge about possible paths especially in unexplored areas. This information facilitates safe movement of troops. While exploring an unexplored area, vegetation cover area need to be checked carefully, because of the tall and dense bushes. During vegetation monitoring various parameters like plant growth, soil moisture, water availability, the need for fertilizers, etc. are observed. All these parameters are necessarily checked to monitor the growth of the plant. The plant growth parameter is reflected from the plant height. In the modern era, smart farming and precision agriculture have been applied, in which monitoring of plant growth-related parameters are optimized and the necessity of any parameter is fulfilled as per the demand and position in the field. In the extension of smart agriculture, the need for the usage of drones arises while monitoring the fields. Drones provide good spatial resolution and can be flown according to the need and application. In this work, the objective is to calculate the plant height using a drone in order to ensure safe troops movement and on other side to monitor the healthy growth of the plant. The application can be helpful for defense as well as civilian purpose. For achieving this, a machine learning-based model has been proposed in which multiple ground control points (GCPs) of different heights have been used to train the model, which results in minimizing the output error. The challenge is to get drone data and manually recorded GCPs height correctly so that the training can be accomplished successfully for better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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An Approach for Transparent Logistic Solution for Life Saving Medicines and Essential Goods using IoT and Blockchain 4

An Approach for Transparent Logistic Solution for Life Savin...

引用

4th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2022

作者： Dasgupta, Agneesh Mukherjee, Shyamapada National Institute of Technology Silchar Department of Electronics and Communication Engineering Assam Silchar788010 India National Institute of Technology Silchar Department of Computer Science and Engineering Assam Silchar788010 India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

IoT devices are increasingly being used in real life to facilitate human life processes such as logistics, through a variety of applications. It has huge demand but the problem it faces security issues. The proposal has presented an autonomous system for the logistics of medicines, vaccines, and of other important and valuable items using Blockchain technology. This work is an amalgamation of IoT and Blockchain technology which is imperative in today's world. The proposed system takes inputs from various sensors through the Raspberry Pi module IoT device. Afterward, the data are passed to the Blockchain smart contract by running a javascript file. These sensor inputs are then used to trigger the smart contract under certain conditions and also create a transaction after the smart contract is triggered. The smart contract has been deployed on the Goerli Ethereum Test Network. The sensors used in the proposed work are the Ultrasonic sensor and Flame sensor. Certain threshold value has been set on both the sensors for which the smart contract gets triggered. The threshold value is modeled based on a flammable and fragile object being transported from a sender to a receiver. Whenever the distance of the object from the container is less than the threshold or if a flame is detected by the sensor, a transaction of Goerli ETH tokens is automatically processed from the sender's account to the receiver's account in a form of compensation. Fig. 1 shows the Raspberry Pi IoT device which was used for the proposed work. © 2022 IEEE.

关键词： Smart contract

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Development of 2.5-Dimensional Hazard Map 11

Development of 2.5-Dimensional Hazard Map

引用

11th IEEE Global Conference on Consumer electronics, GCCE 2022

作者： Yoshimura, Kazuma Kaneko, Takumi Fujisawa, Yoshinori National Institute of Technology Nagano College Department of Electronics and Computer Science Nagano Japan National Institute of Technology Nagano College Department of Engineering Nagano Japan

ISBN: (纸本)9781665492324

This research is developing a 2.5-dimensional (2.5D) version of hazard maps to enable visually impaired people to use hazard maps, starting with the flooding in Nagano Prefecture in 2019. In previous research, we defined a 2.5D hazard map to express by cutting an acrylic board. This study is developing a web application to create the data for the need to make a 2.5D hazard map. © 2022 IEEE.

关键词： Disaster prevention

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Designing and Improving a Real-Time Trust-Based Security System for IoT Healthcare Monitoring

Designing and Improving a Real-Time Trust-Based Security Sys...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sureshkumar, S. Jadhav, Sulaxan Yeruva, Ajay Reddy Wirayasa, I. Ketut Adi Mishra, Krishna Nand Ul Islam, Atowar P. A. College of Engineering and Technology Department of Computer Science and Engineering Pollachi642002 India Dy Patil International University School of Interdisciplinary Studies and Research Pune Akurdi411044 India 1326 Hopyard Road Apt #62 PleasantonCA94566 United States Pradita University Department of Computer Science Indonesia Khwaja Moinuddin Chishti Language University Department of Computer Science and Engineering Uttar Pradesh Lucknow226013 India University of Science and Technology Meghalaya Department of Computer Science and Electronics Ri-Bhoi Techni city Killing Road Meghalaya Baridua793101 India

ISBN: (纸本)9798350373783

Patient monitoring has taken on a new dimension as a result of the Internet of Things' (IoT) incorporation into the healthcare system. IoT-enabled medical equipment have numerous sensors that can collect a variety of patient data. The gateway sensor amalgamates this data before transmitting it to cloud storage. The information is then processed on the cloud and sent to the doctor's interface. IoT usage in healthcare is widespread, but security is still a major obstacle. Patient safety is put at risk due to the possibility of data alteration, fraudulent information insertion, and unauthorised access. Security is emphasized as a primary design objective in this project focused on IoT-based healthcare systems, recognizing the seriousness of associated risks. Our method revolves around a suggested digitally signed trust analysis security mechanism to strengthen end-to-end security and guard against harmful behaviours. The article presents a strong system that is intended to fend off hostile attacks and protect private information from outside dangers. Thorough simulations confirm that the suggested framework is successful in improving the security of Internet-of-things-based healthcare systems. © 2024 IEEE.

关键词： Gateways (computer networks)

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Comparative Evaluation of EEG Feature Fusion Techniques for Emotion Classification 15

Comparative Evaluation of EEG Feature Fusion Techniques for ...

引用

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

作者： Ameta, Durgesh Gupta, Ashish Singh, Harshil Deep Uzir, Didrikhya Kothari, Rhythm Bose, Rapolu Subhash Chandra Singh, Harjot Behera, Laxmidhar Iksmha Center Indian Institute of Technology Mandi Mandi India Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur India Indian Institute of Information Technology Department of Computer Science HP Una India Cair Indian Institute of Technology Mandi Mandi India Indian Institute of Information Technology Department of Electronics HP Una India Indian Institute of Technology Mandi Mandi India

ISBN: (纸本)9798350370249

Brain-computer interface (BCI) system-based emotion classification is an intriguing topic for research. Feature extraction is an essential step in these systems, usually carried out by a deep neural network or hand-crafted features. Studies in this area frequently highlight the significance of a small set of features;a precise comparison of different proposed features is still necessary. To classify emotions from EEG data, we attempt an exploratory evaluation of a number of EEG features, including those in the time domain, frequency domain, auto-encoded, and deep features, using the DENS dataset. Baseline techniques like Random Forest (RF), XgBoost (XGB), and KNN are used to compare feature performance. The results of this study shed light on the possibility of using these EEG features to identify emotional states. Compared to the previous results on the same dataset, our results show accuracies of 82.10% and 81.33% for valence and arousal, an increase of 29.63% and 18.82%, respectively. © 2024 IEEE.

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Assessing Social Media's Impact on Mental Health: A Gaussian Naive Bayes Approach

Assessing Social Media's Impact on Mental Health: A Gaussian...

引用

Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： S. Nanditha Helen K. Joy Manjunnth R. Kounte R. Sridevi A. Electa Alice Jayarani D. Vanusha Department of Computer Science CHRIST University Bangalore India School of Electronics and Communication Engineering REVA University Bangalore India Department of Electronics and Communication Engineering KS Institute of Technology Bangalore India Department of Computer Science and Engineering SRM Institute of Technology Chennai India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In recent times, social media has gained immense popularity. However, there is a growing concern among individuals regarding its impact on mental well-being. Extensive research has been conducted to unravel the intricate connection between the use of social media and mental health. This study thoroughly examines the existing literature by incorporating various studies that cover topics such as machine learning for analysing depression-related sentiments and the effects of social media on specific demographic groups. The aim is to develop a comprehensive understanding of the relationships between these factors. In order to gain a deeper understanding of mental health indicators, a dataset consisting of 481 records was analysed using various techniques including pre-processing, correlation analysis, data visualisation, and Gaussian Naive Bayes predictive modelling. Based on our findings, it appears that individuals who engage with social media may experience negative impacts on their mental well-being. During our investigation, we conducted tests on different machine learning models and discovered that Gaussian Naive Bayes yielded the highest level of accuracy. The presence of multicollinearity among independent variables emphasises the importance of being cautious when interpreting research findings. This study makes a valuable contribution to our understanding of the complexities involved in using social media platforms while managing mental health issues. By focusing on the intersection of technology and psychiatric illnesses, it lays the groundwork for future research and interventions in this important area.

关键词： Analytical models Correlation Accuracy Social networking (online) Data visualization Mental health Machine learning Predictive models Data models Bayes methods

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An Effective Scheme to Mitigate Blackhole Attack in Mobile Ad Hoc Networks 26th

引用

26th annual International Conference on Advanced Computing and Communications, ADCOM 2020

作者： Shukla, Mukul Joshi, Brijendra Kumar Department of Information Technology Shri G. S. Institute of Technology & Science Indore India Electronics & Telecommunication and Computer Engineering Military College of Telecommunication Engineering MHOW454431 India

ISBN: (纸本)9789811900181

MANET refers to a mobile ad hoc network, which is self-configured without having a fixed base. It has been used in various fields for various purposes like military, local conferences, and the movable of information. However, due to the lack of built-in security, safety is a significant concern in the MANET. There are various types of attacks which are possible on MANETs. One of the attack is a Blackhole attack. It is an active attack in which a malicious node shows itself as the shortest route and absorbs the packet just like a blackhole does in-universe. In this paper, a proposed technique has used a trust-based fuzzy method based on auditing of energy, the Trust of a neighbouring node, check for the integrity of packets, and authentication of the Node Member. Trust values in the fuzzy logic range from 0 to 1. If node trust value is higher than or equal to 0.6, then the node is trusted, and its type of node taken in our scenario for communication between source to destination. If the node trust rate on the routing table is less than 0.6, that means the node is a blackhole node, and its type node does not consider a safe route, in this paper proposed a method, i.e., Trust-based Fuzzy Ad hoc On-Demand Distance Vector (TFAODV), to an attack scenario. This paper is improved results in terms of throughput, packet delivery ratio, end-to-end delay, shows the throughput improvement, and found to be 1441 kbps, packet delivery ratio enhancement of 57.10%, and delay decrease of 52% from Blackhole Ad hoc On-Demand Distance Vector (BAODV). Therefore, the proposed protocol covers the way and possesses the potential to secure the MANET. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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Classification of Tea Leaf Diseases Using Convolutional Neural Network 26th

Classification of Tea Leaf Diseases Using Convolutional Neur...

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26th annual International Conference on Advanced Computing and Communications, ADCOM 2020

作者： Chakraborty, Subham Murugan, R. Goel, Tripti Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar788010 India Department of Electronics and Communication Engineering National Institute of Technology Silchar Assam Silchar788010 India

ISBN: (纸本)9789811900181

Assam is the highest tea-producing state in India. The economy of this state is greatly dependent on the cultivation and productivity of tea. The biggest challenge to tea growers is to produce tea without microbial or pesticide damage. Since leaves are the harvested product in tea, so leaf diseases play an important role. Lack of awareness and care to leaves causes unfavorable impacts on plants, product quality, and quantity get reduced. The symptoms of the disease can be observed on the leaves. The leaf shows symptoms by changing color or showing spots on it. The identification of these diseases is made manual, which can consume more time or may be costly. The idea is to identify and classify the diseases accurately from leaf images automatically. The Convolutional Neural Network is being proposed in this study, which has classified the diseases with an accuracy of 92.59% and is more accurate than the prevailing classifiers like Support Vector Machine and K-Nearest Neighbors for this specific purpose. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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