2023 40th National Radio Science Conference (NRSC)最新文献_第3页

Optical Electrical Modeling of Band Gap Tunable Triple Cation Perovskite 带隙可调三阳离子钙钛矿的光电建模

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10153045

Karim Sewilam, M. Fedawy, Hesham Aly

引用次数: 0

Deep Learning-aided Channel Estimation For Universal Filtered Multi-carrier Systems 通用滤波多载波系统的深度学习辅助信道估计

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10152951

Mohab Youssef, Michael Ibrahim, Bassant Abdelhamid

{"title":"Deep Learning-aided Channel Estimation For Universal Filtered Multi-carrier Systems","authors":"Mohab Youssef, Michael Ibrahim, Bassant Abdelhamid","doi":"10.1109/NRSC58893.2023.10152951","DOIUrl":"https://doi.org/10.1109/NRSC58893.2023.10152951","url":null,"abstract":"In this paper, a novel channel estimation technique is proposed for Universal Filtered Multi-Carrier (UFMC) systems. The proposed technique employs Deep Learning (DL) models which utilize the, commonly discarded, odd-indexed samples of the received signal in order to enhance the channel estimation. Three DL models with different sets of input features are proposed. The three proposed DL models were trained and then deployed into a UFMC system to evaluate their performance. The performance metric for the training stage is the Normalized Mean Squared Error (NMSE) between the estimated channel and actual channel coefficients. For deployment stage, both NMSE and Bit Error Rate (BER) are chosen as performance metrics. The proposed models are compared versus conventional Least Square (LS) channel estimator. The results show that the proposed DL-models outperform the LS channel estimator for various Signal to Noise Ratio (SNR) even for channel models which are different from the one used for training. The SNR gains of utilizing the proposed models are 5-6dBs and 2-3dBs on average for NMSE and BER, respectively.","PeriodicalId":129532,"journal":{"name":"2023 40th National Radio Science Conference (NRSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125542911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Software Solution for Three-Dimensional Dynamic Contrast-Enhanced Ultrasound Imaging using Pulse Inversion Spectral Deconvolution 三维动态对比增强超声成像脉冲反演光谱反褶积软件解决方案

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10152907

Mawia Khairalseed, K. Hoyt

{"title":"Software Solution for Three-Dimensional Dynamic Contrast-Enhanced Ultrasound Imaging using Pulse Inversion Spectral Deconvolution","authors":"Mawia Khairalseed, K. Hoyt","doi":"10.1109/NRSC58893.2023.10152907","DOIUrl":"https://doi.org/10.1109/NRSC58893.2023.10152907","url":null,"abstract":"The use of microbubble (MB) contrast agents and specialized ultrasound (US) techniques during contrast-enhanced ultrasound (CEUS) imaging has been an effective way to visualize blood vessels. Various transmit pulsing and postprocessing schemes are typically used for CEUS imaging to extract the nonlinear MB waveform from the echo signal. However, a new software solution called pulse inversion spectral deconvolution (PISD) has been introduced that can separate tissue and MB signal components without the need for a specific pulse selection. The study detailed herein aims to describe a novel three-dimensional (3-D) PISD-based CEUS imaging system and method. US data was collected using a research scanner (Vantage 256, Verasonics Inc) equipped with a 1024-element matrix array transducer (Vermon) and a vascularized phantom (Model ATS 524, Sun Nuclear) linked serially with a flow pump and mixing chamber filled with a MB contrast agent (Definity, Lantheus Medical Imaging) in water solution. 3-D PISD-based CEUS images were produced offline by custom MATLAB software (Math Works Inc), which involved using a pair of Gaussian derivative functional filters applied to backscattered US channel data before envelope detection and volume reconstruction. A contrast-to-noise ratio (CNR) measure was used to quantify contrast enhancement between PISD and standard B-mode CEUS images of the perfused phantom material. Results showed that 3-D PISD-based CEUS imaging provided a 40 dB increase in vessel enhancement compared to B-mode CEUS images. Overall, these preliminary in vitro findings suggest that 3-D CEUS with PISD postprocessing has potential as a valuable addition to the current vascular imaging techniques.","PeriodicalId":129532,"journal":{"name":"2023 40th National Radio Science Conference (NRSC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124486125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network Intrusion Detection CNN Model for Realistic Network Attacks Based on Network Traffic Classification 基于网络流量分类的网络入侵检测CNN模型

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10152872

Alla Abd El-Rady, Heba Osama, Rowayda Sadik, Hesham El Badwy

{"title":"Network Intrusion Detection CNN Model for Realistic Network Attacks Based on Network Traffic Classification","authors":"Alla Abd El-Rady, Heba Osama, Rowayda Sadik, Hesham El Badwy","doi":"10.1109/NRSC58893.2023.10152872","DOIUrl":"https://doi.org/10.1109/NRSC58893.2023.10152872","url":null,"abstract":"Network intrusion detection has an important role in providing security to networks and computer systems. It applies different artificial intelligence technologies in order to improve performance against various cyber-attacks. Applying Deep Learning (DL) techniques has a considerable impact compared to using traditional Machine Learning (ML) methods. Recently, Convolutional Neural Network (CNN) has been widely used by researchers to enhance Intrusion Detection Systems (IDSs). This paper aims to build a customized CNN model to improve the accuracy of IDSs. The study involves comparing the results obtained from the proposed CNN model with those obtained from Random Forest which is a well-known machine learning technique utilized frequently in IDSs. The performance of both models was evaluated using standard measurements such as accuracy and F-measure. Two datasets were used, UNSW-NB15 and CSE-CICIDS2018, to demonstrate the efficacy of our proposed model. The proposed CNN model achieved better results than the Random Forest algorithm. A comparison with existing recent IDSs was carried out. The result of this study provides insights and proves the effectiveness of using CNNs for IDSs and helps in identifying the best approach for building efficient and accurate CNN based IDSs with the accuracy of 99.18% using CSE-CICIDS2018 dataset and 99.70% using UNSW-NB15 dataset.","PeriodicalId":129532,"journal":{"name":"2023 40th National Radio Science Conference (NRSC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123241213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Proposed Technique for Improving SNR in Pulse Compression Radars 一种提高脉冲压缩雷达信噪比的方法

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10153003

Alaa G. Zahra, Ahmed Youssef, F. Ahmed, Wael Mehany

{"title":"A Proposed Technique for Improving SNR in Pulse Compression Radars","authors":"Alaa G. Zahra, Ahmed Youssef, F. Ahmed, Wael Mehany","doi":"10.1109/NRSC58893.2023.10153003","DOIUrl":"https://doi.org/10.1109/NRSC58893.2023.10153003","url":null,"abstract":"From the birth of radars, researchers are racing to provide the best solutions for enhancing radar systems detectability over long ranges by improving the signal-to-noise ratio (SNR). As the world witnesses a huge progress in all fields, radar targets become small in size, and they often have a structure designed according to the stealth topology. As a result, their detection becomes a hard operation. Therefore, there is an insistent need for techniques that increase the system processing gain, to boost the its detection performance. Almost all proposed methods for increasing the radar processing gain require huge physical resources in the radio frequency front-end, or in the digital signal processing hardware implementation. Therefore, these solutions seem to be expensive and sometimes not practical. In this article, we are introducing a technique for increasing the radar system processing gain operating with phase-coded signals, which in turn enhances the system SNR and its detection capability. The proposed approach depends on using $n$ samples per the received signal subpulse, using two coherent pulse intervals for taking decisions. In addition, the required hardware resources for the practical implementation of the suggested method is near to the conventional processing that depends on one sample per subpulse. The method methodological model will be discussed, and the simulation results are presented to ensure the advantage of the proposed technique.","PeriodicalId":129532,"journal":{"name":"2023 40th National Radio Science Conference (NRSC)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128345931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications 基于IDS_MDL算法的物联网入侵检测预测性能分析

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10153000

Walaa Adel Mahmoud, M. Fathi, Hesham M. El-Badawy, R. Sadek

{"title":"Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications","authors":"Walaa Adel Mahmoud, M. Fathi, Hesham M. El-Badawy, R. Sadek","doi":"10.1109/NRSC58893.2023.10153000","DOIUrl":"https://doi.org/10.1109/NRSC58893.2023.10153000","url":null,"abstract":"In the last year, many modern attacks have targeted Internet of Things (IoT) devices. IoT botnet attacks are the most common. Despite the plethora of detection and prevention systems for cyberattacks, a security mechanism for IoT settings is still required since these devices have certain restrictions that make it difficult to properly implement the detection system. These memory resources are limited to processing, storing, and calculating. An effective defense against cyberattacks is an intrusion detection system (IDS). To develop the rules for IDS_MDL-IoT, we used the contemporary botnet attack dataset. Because IoT devices have limited resources, it is difficult to employ all attack patterns in the initial anomaly dataset in traditional anomaly-based IDS. Therefore, in this study, the tested ML model resulting in NB has proven to be the best classification algorithm to classify the risk of attack with 99.7% accuracy predicting from a time-speed of 3 seconds, and the tested DL model resulting in RNN has proven to be the best classification algorithm to classify the risk of attack with 99% accuracy predicting from a time-speed of 8 seconds.","PeriodicalId":129532,"journal":{"name":"2023 40th National Radio Science Conference (NRSC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123543636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimized Wireless Video Transmission in a Smart Agricultural Solutions 智能农业中优化的无线视频传输解决方案

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10153047

Mohamed Naeem, M. Aboul-Dahab

引用次数: 0

High Gain Wideband Non-uniform Ku-band Array Antenna 高增益宽带非均匀ku波段阵列天线

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10152894

Ibrahim S. Mohamed, A. Abdelraheem, M. Abdalla

引用次数: 0

Stress Detection in the Wild: On the Impact of Cross-Training on Mental State Detection 野外压力检测:关于交叉训练对心理状态检测的影响

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10153050

Mona Mamdouh, Rojaina Mahmoud, Omneya Attallah, A. Al-Kabbany

{"title":"Stress Detection in the Wild: On the Impact of Cross-Training on Mental State Detection","authors":"Mona Mamdouh, Rojaina Mahmoud, Omneya Attallah, A. Al-Kabbany","doi":"10.1109/NRSC58893.2023.10153050","DOIUrl":"https://doi.org/10.1109/NRSC58893.2023.10153050","url":null,"abstract":"This research is concerned with the following question: How a model trained on mental state detection using stress inducer X would perform on detecting a stress stimulated by inducer Y? This is a scenario that can be faced often in real-life. In vision applications, detection in the wild usually implies a real-life setting with minimal constraints. In this research, we investigate the generalization between a classical stress inducer, namely, mental arithmetic stressor (MAE), and the stress induced by immersive technologies, particularly virtual reality (VR)-VR-induced stress. On a group of three participants, we acquired EEG recordings following their exposure to the two types of stressors mentioned above. Using multiple machine learning (ML) classifiers, we show that even basic ML models, when trained to differentiate between the MAE stressor and the non-stress state, can generalize well when tested on differentiating between VR-based stress and the non-stress state. Significant cross-detection performance is reported using accuracy, precision, recall, and F1-score. This research provides preliminary insights on the capacity of machine learning models to generalize well in the contexts of mental state detection. 1","PeriodicalId":129532,"journal":{"name":"2023 40th National Radio Science Conference (NRSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129518878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-Phases Intelligent Reflectarray Antenna Using Liquid-Flow Control for Sub-6 GHz 5G Communications 基于液流控制的三相智能反射天线用于Sub-6 GHz 5G通信

2023 40th National Radio Science Conference (NRSC) Pub Date : 2023-05-30 DOI: 10.1109/NRSC58893.2023.10152902

S. Zainud-Deen, H. Malhat, Esraa A. El-Refaey, M. M. Badawy

引用次数: 0