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GNU Radio-Based SSK MIMO System: From Concept to Reality

GNU Radio-Based SSK MIMO System: From Concept to Reality

引用

International Conf on Electrical and Electronic Engineering (ICEEE)

作者： Raed Mesleh Omar Hiari Ayat Alolimat Mustafa Shawaqfeh Princess Sumaya University for Technology Amman Jordan Computer Engineering Department German Jordanian University Amman Jordan Electrical Engineering Department German Jordanian University Amman Jordan

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

The inherent characteristics of space shift keying (SSK) had generated substantial research interest in the domain of multiple-input multiple-output (MIMO) systems, resulting in the development of hardware-based implementations. With the emergence of software-defined radio (SDR) technologies, radios could be implemented through software solutions. One prominent open-source SDR toolkit, GNU Radio, offered a comprehensive suite of signal processing blocks tailored for this purpose. In this study, a 2 × 2 SSK MIMO system was implemented using GNU Radio, inclusive of the deployment of advanced algorithms on a commercially available SDR platform for real-time transmission and reception. To assess the effectiveness of the implementation, meticulous comparisons were made between measurement results and simulation outcomes, revealing a high degree of accuracy in matching for a Rician fading channel characterized by a $K$ -factor of 2.5.

关键词： Software algorithms Rician channels signal processing algorithms signal processing Transceivers Software Real-time systems Trajectory Synchronization Software radio

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CNNRF-Ensemble: A Multifaced Approach For Predicting White Spot Syndrome Virus In Shrimp Farming

CNNRF-Ensemble: A Multifaced Approach For Predicting White S...

引用

International conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Kazi Tanvir Sadi Al Huda Shaiful Ajam Opee Sayem Kabir Asraf Ali Touhid Bhuiyan Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Artificial Intelligence Research and Innovation Lab Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria VA USA

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries, filling the rapid growth demand, especially in Bangladesh. However, the White Spot Syndrome Virus(WSSV) creates and constantly threatens the global shrimp population, impacting the economic and ecological systems. Recently, researchers have tried to predict disease, but no studies have achieved significant accuracy using ML and DL algorithms. In this study, we have proposed an optimized CNNRF-Ensemble model for predicting WSSV using multifaceted data, including geographical features, environmental attributes, farm characteristics, operational practices, and socioeconomic indicators. Our proposed model achieved 95.714% accuracy in sequential model and afterwards, combining with RandomForestClassifier in Ensemble learning we obtained 99.99% accuracy. Our study suggested deep learning as an accurate, easy and low-cost method to predict shrimp WSSV, which helps to predict viruses and create awareness of shrimp firming.

关键词： Deep learning Accuracy Biological system modeling signal processing algorithms Ensemble learning Convolutional neural networks Socioeconomics Viruses (medical) Farming Diseases

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Performance of RIS-Assisted Two-Way Communications with Phase Noise

Performance of RIS-Assisted Two-Way Communications with Ph...

引用

International conference on advanced Engineering Theory and Applications, AETA 2022

作者： Tu, Lam-Thanh Nguyen, Tan N. Tran, Phuong T. Van Huynh, Van Trung Duy, Tran Nguyen, Nhat-Tien Voznak, Miroslav Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh city Viet Nam Wireless Communications Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh city Viet Nam Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh city Viet Nam Posts and Telecommunications Institute of Technology Ho Chi Minh city Viet Nam VSB—Technical University of Ostrava Ostrava Czech Republic

ISBN: (纸本)9789819987023

The performance of two-way full-duplex communications with the help of reconfigurable intelligent surface (RIS) under phase noise is investigated in this work. Particularly, we study the outage probability, the average rate of each end-user, and the average sum rate of the whole network under both optimal phase-shift and random phase-shift design. Simulation results via the Monte-Carlo method are given to confirm the advantages of the optimal design compared with the random one and to unveil the impact of some essential parameters such as the number of RIS elements, and the number of quantization bits on the system performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Phase noise

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Fault Diagnosis in Belts Using signal processing Techniques and Machine Learning 1st

Fault Diagnosis in Belts Using Signal Processing Techniques ...

引用

1st International conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Kirankumar, M.V. Rajeshwari Nishmitha Kumari, Pavitra Raghavendra, B.K. Vyankatesh, Kulkarni Sourabh Karnataka Moodbidri574225 India Electrical and Electronics Engineering Govternment Polytechnic Karnataka Koppal583231 India KPIT Technologies Pune India

ISBN: (纸本)9783031756047

In the era of modern industries, use of advanced machines has increased the productivity of plants. Defect of such complex machines can increase the downtime and hence reduce productivity and ultimately the profit. Proper maintenance of systems is very crucial to ensure trouble free operation of machines. Predictive Maintenance techniques are those techniques which are used for identification of fault in its early stage. This avoids the sudden unavailability of machines and reduces the downtime. Predictive Maintenance techniques using vibration analysis is one of the very popular methods of Condition Monitoring. These techniques are evolving rapidly due to their efficient detection of fault in its incipient stage. However, non-stationary of the vibration signal produced particularly in belts in belt drive systems using conventional techniques of signal processing poses challenges. In this project work, the application of advanced signal processing techniques is investigated for the automatic fault detection in belts. This project work is focused on developing a novel concept of vibration signal processing for the automatic fault detection in belts using wavelet analysis and the belt condition classification and prediction using Machine Learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Condition Monitoring Fault Diagnosis Machine Learning

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Power Efficient Real-Time Traffic signal Classification for Autonomous Driving Using FPGAs

Power Efficient Real-Time Traffic Signal Classification for ...

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International conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Rashed Al Amin Md Shahi Amran Hossain Leon Tjard Schmid Veit Wiese Roman Obermaisser Institute for Embedded Systems University of Siegen Siegen Germany

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

The significant advancements in research within the domain of advanced Driver-Assistance Systems (ADAS) have been significantly propelled by the integration of Artificial Intelligence (AI) and Deep Learning (DL). The detection and classification of traffic signals are pivotal for ensuring driver safety and comfort, prompting the development of numerous detection and classification methodologies in recent times. Convolutional Neural Networks (CNNs) and Field Programmable Gate Arrays (FPGAs) have individually demonstrated notable efficacy in signal processing, image detection, and image classification tasks. However, the amalgamation of CNNs and FPGAs for traffic signal classification offers enhanced flexibility, accuracy, and resource efficiency. This study proposes a software-hardware co-design approach for the development of a resource-efficient real-time traffic signal classification system tailored for FPGAs utilizing the CNN algorithm. The software implementation has been carried out within the TensorFlow framework, while the hardware implementation is executed via the Xilinx Vivado 2023.1 platform and rigorously evaluated using the Xilinx ZCU102 FPGA board. Experimental findings demonstrate that the traffic signal classification accuracy can attain approximately 99.98% at a frame rate of 84139 frames per second (FPS) on the FPGA platform, with a power consumption of 4.4W. The exceptionally low latency and heightened accuracy exhibited by the proposed system render it highly advantageous for ADAS and other safety-critical applications.

关键词： Accuracy Software algorithms Pattern classification signal processing algorithms Real-time systems Software Safety Convolutional neural networks Artificial intelligence Field programmable gate arrays

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Model Switching Detection-Aided Adaptive Transition Probability Matrix IMM Algorithm

Model Switching Detection-Aided Adaptive Transition Probabil...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Youwang Chen Yunkai Deng Weiming Tian Radar Technology Research Institute Beijing Institute of Technology Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing Advanced Technology Research Institute Beijing Institute of Technology Jinan China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronic Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In conventional interactive multiple model (IMM) algorithm, the fixed transition probability matrix results in slow model switching and low tracking accuracy. Existing adaptive transition probability IMM algorithms still suffer from large errors during model switching. This paper proposes a model switching detection-aided adaptive transition probability matrix IMM algorithm. To deal with model switch time delay, a transition probability correction function is established, capable of dynamically and rapidly adjusting the transition probabilities. And a model switching detection method is proposed to improve tracking accuracy during model switching. By the numerical simulation, the method's efficacy is substantiated and confirmed.

关键词： Adaptation models Accuracy Heuristic algorithms Delay effects Switches Numerical simulation Data processing Numerical models

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Retrieval-Augmented Multilingual Citation Generation

Retrieval-Augmented Multilingual Citation Generation

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xun Liang Simin Niu Sensen Zhang Zhiyu Li Xuan Zhang Bo Wu Feiyu Xiong Bo Tang Hanyu Wang Shichao Song Mengwei Wang Jiawei Yang School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Retrieval-augmented citation generation (RACG) helps users trust the large language model output by retrieving evidence from reliable sources. However, most current RACG research focuses on single-language tasks, particularly in English, and overlooks the need for cross-lingual evidence retrieval and utilization in real-world applications. To address this issue, we introduce a plug-and-play Retrieval-Augmented Multilingual Citation Generation method (RAMCG) which uses a multilingual retriever to identify relevant evidence from a multilingual knowledge base. The evidence is then combined with the query and processed by a multilingual citation generator. The result is citations that are both accurate and comprehensive. Experiments show that RAMCG outperforms baseline methods in multilingual citation generation and is well-suited for practical use.

关键词： Measurement Generative AI Large language models Knowledge based systems signal processing Generators Acoustics Multilingual Reliability Speech processing

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Basic EEG signal processing and Movement Classification for Mind-Controlled Prosthetic Arm

Basic EEG Signal Processing and Movement Classification for ...

引用

International conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Manikandan J Mangaiyarkarasi V Saritha G Suganya M Gayathri R J Swathi Murali Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

This study presents a preliminary investigation into the feasibility of controlling a prosthetic arm using electroencephalography (EEG) signals. EEG data was collected and pre-processed to isolate relevant brain activity associated with hand movements. Basic feature extraction techniques were applied to quantify these patterns, followed by a simple classification algorithm to distinguish between hand open and closed states. While demonstrating the potential of EEG-based control, this research highlights the need for advanced signal processing, robust feature engineering, and sophisticated machine learning models to achieve accurate and reliable prosthetic control.

关键词： Electric potential Machine learning signal processing Reliability engineering Feature extraction Control systems Brain modeling Electroencephalography Classification algorithms Prosthetics

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Long Range Detection of Emanation from HDMI Cables Using CNN and Transfer Learning

Long Range Detection of Emanation from HDMI Cables Using CNN...

引用

Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Bari, Md Faizul Chowdhury, Meghna Roy Sen, Shreyas Purdue Univ Elmore Family Sch ECE W Lafayette IN 47907 USA

ISBN: (纸本)9798350396249

The transition of data and clock signals between high and low states in electronic devices creates electromagnetic radiation according to Maxwell's equations. These unintentional emissions, called emanation, may have a significant correlation with the original information-carrying signal and form an information leakage source, bypassing secure cryptographic methods at both hardware and software levels. Information extraction exploiting compromising emanations poses a major threat to information security. Shielding the devices and cables along with setting a control perimeter for a sensitive facility are the most commonly used preventive measures. These countermeasures raise the research need for the longest detection range of exploitable emanation and the efficacy of commercial shielding. In this work, using data collected from 3 types of commercial HDMI cables (unshielded, single-shielded, and double-shielded) in an office environment, we have shown that the CNN-based detection method outperforms the traditional threshold-based detection method and improves the detection range from 4 m to 22.5 m for an iso-accuracy of similar to 95%. Also, for an iso-distance of 16 m, the CNN-based method provides similar to 100% accuracy, compared to similar to 88.5% using the threshold-based method. The significant performance boost is achieved by treating the FFT plots as images and training a residual neural network (ResNet) with the data so that it learns to identify the impulse-like emanation peaks even in the presence of other interfering signals. A comparison has been made among the emanation power from the 3 types of HDMI cables to judge the efficacy of multi-layer shielding. Finally, a distinction has been made between monitor contents, i.e., still image vs video, with an accuracy of 91.7% at a distance of 16 m. This distinction bridges the gap between emanation-based image and video reconstruction algorithms.

关键词： emanation electromagnetic compatibility CNN transfer learning ResNet50 HDMI EMSEC signal processing

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High-Resolution Range Profile Target Recognition with Neuromorphic ADCs and Spiking Neural Networks

High-Resolution Range Profile Target Recognition with Neurom...

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IEEE National conference on Radar

作者： Sanjaya Herath Matthew R. Ziemann Kevin Wagner Christopher A. Metzler University of Maryland College Park MD Naval Research Laboratory Washington DC DEVCOM Army Research Laboratory Adelphi MD

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

Traditional analog-to-digital converters (ADCs) often struggle to balance high sampling rates with power efficiency, limiting their effectiveness in advanced radar and communication systems. Neuromorphic ADCs capture samples only when a signal crosses a specific threshold. This asynchronous sampling strategy effectively compresses incoming datastreams, enabling neuromorphic ADCs to achieve higher sampling rates at reduced power consumption compared to conventional ADCs. However, existing algorithms are poorly suited for processing the asynchronous samples. Conventional techniques like matched filters are inapplicable and most established deep learning algorithms expect regularly sampled data. This work introduces a spiking neural network (SNN) architecture specifically designed for processing asynchronous radar samples. Our novel approach is applied to Radar High-Resolution Range Profile (HRRP) based target classification. Remarkably, our experiments demonstrate that combining a neuromorphic ADC with an SNN achieves performance on par with high-sample-rate conventional ADCs paired with Convolutional Neural Networks (CNNs) while reducing the overall sampling rate by more than 95%.

关键词： Deep learning Power demand Neuromorphics Target recognition signal processing algorithms Radar Throughput

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