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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Nigam, Ritika Kundu, Arpan Yu, Xinrui Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (数字)9781665480093

ISBN: (纸本)9781665480109

This paper presents the design and implementation of the Machine Vision Surveillance System Artificial Intelligence (MaViSS-AI) for Covid-19 Norms using jetson nano. This system is designed to be cost-effective, accurate, efficient, and secure. The proposed system tracks and counts humans for monitoring social distancing and detects face masks using object detection methods. We used YOLO as an object detection method and neural network to detect a person and count them. And for social distancing monitoring the concept of the centroid is based on calculating the distance between pairs of centroids, and thus checking whether there are any violations of the threshold or not. To detect the face mask, a YOLO V4 deep learning model is used as the mask detection algorithm. The system also raises alerts when any suspicious event occurs. Given this alert, security personnel can take relevant actions. This research aims to provide a holistic approach to overcoming the real-time challenges encountered during the monitoring of Covid-19 norms. © 2022 IEEE.

关键词： Object detection

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Design Flow of mmWave Radar and Machine Vision Fusion for Pedestrian Collision Warning

Design Flow of mmWave Radar and Machine Vision Fusion for Pe...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Graves, Kaitlyn Kanwal, Madiha Yu, Xinrui Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper presents a millimeter wave (mmWave) radar and Machine Vision fusion system to alert drivers of potential pedestrian collisions. The system is composed of two subsystems, the mmWave Pedestrian Localization subsystem and the Machine Vision Pedestrian Classification subsystem. The mmWave Pedestrian Localization subsystem obtains the relative location of the pedestrians using a mmWave radar sensor while the Machine Vision Pedestrian Classification subsystem uses Histogram of Oriented Gradients and Support Vector Machine algorithms to classify pedestrians in a camera's field-of-view. The two-layer pedestrian detection design protects the system from any miss detections within a single subsystem. By utilizing mmWave technology with Machine Vision, the safety operation of cars and pedestrian safety can be increased. The proposed system utilizes Texas Instrument's AWR1642BOOST mmWave Radar with the high computing power of NVIDIA's Jetson Nano © 2022 IEEE.

关键词： computer vision

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Tackling Multiple Security Threats in an IoT Environment

Tackling Multiple Security Threats in an IoT Environment

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Gromov, Mikhail Arnold, David Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

While in traditional computing there are many cybersecurity flaws that have been solved, in IoT the computing model differs from regular computing, and can be changed based on the application. Due to this, some new flaws are introduced in IoT, and some old flaws need new solutions. While there have been investigations into individual flaws, sometimes proposed solutions can also create new flaws or a new attack vector for another attack. Therefore, it is important to consider the totality of an application in order to decide on what security measures need to be taken, and how they can be implemented in the design. In this paper, a few common IoT system styles including medical devices based on IoT, IoT devices in home networks, and wide area IoT networks are analyzed. This analysis uses a simple method to consider the vulnerabilities of each system individually and determine how to best respond to these weaknesses. The weaknesses analyzed include Distributed Denial of Service, public access, device identification, and variability of hardware. © 2022 IEEE.

关键词： Cybersecurity

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Design Flow and Implementation of a Vision-Based Gesture-Controlled Drone

Design Flow and Implementation of a Vision-Based Gesture-Con...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Findelair, Arthur Yu, Xinrui Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

The increasing efficiency of complex Neural Network architecture and the continuous improvement of embedded edge computing has reached a point that allows the deployment of advanced computer vision tasks on some of the most critical embedded applications, such as aerial drones. While similar applications were already possible by moving heavy processing on a ground station, an autonomous and centralized system significantly improves usability and security. The machine is thus self-sufficient and less prone to network attacks. Three main challenges stand-out during the deployment of our complex gesture recognition pipeline: (1) allowing user-defined controls, (2) ensuring robustness, and (3) on-board deployment. These challenges are tackled through handcrafted features to avoid the curse of dimensionality, neural network optimization on GPUbased companion computers, and data augmentation to cover reallife edges cases such as partial inputs. © 2022 IEEE.

关键词： Drones

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Enhancing Energy Management System for a Hybrid Wind Solar Battery Based Standalone Microgrid

Enhancing Energy Management System for a Hybrid Wind Solar B...

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2023 IEEE International Conference on Environment and electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Aghmadi, Ahmed Hussein, Hossam Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350347432

This paper introduces a highly efficient energy management system for a microgrid that combines PV system, wind turbine, and battery. The study presents an effective energy management system specifically designed for a small-scale hybrid microgrid, focusing on the development of solar and wind energy conversion systems and battery storage systems. Power electronic converters and control algorithms are implemented to evaluate the microgrid's operation. The energy management system plays a crucial role in maintaining power balance amidst varying renewable energy generation and load demand conditions. The microgrid is a testing platform to evaluate control algorithms, energy management systems, and various test scenarios. It provides a controlled environment for assessing the performance and efficiency of these systems. The efficient energy management system comprises photovoltaic (PV) modules, wind turbines, and batteries. The proposed strategy effectively manages the switching between the PV panel and the battery storage system while prioritizing power generation from the wind turbine. The efficacy and success of this strategy in microgrid control under various operational circumstances are demonstrated through MATLAB simulation results. © 2023 IEEE.

关键词： Wind power

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Auditory Brainwave Entrainment System using Time-Series Analysis of EEG Signal

Auditory Brainwave Entrainment System using Time-Series Anal...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Kaur, Simrat Krishnan, Rajesh Kumar Yu, Xinrui Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper is focused on improving people's general inability to quality sleep. We designed a feedback system that can process brainwaves in the form of time-series EEG data and convert them into RGB color-coded based topological map-like images. The system then uses CNN architecture based on 2D convolution with ReLU activation functions and dense layers for image and frequency classification to detect the EEG frequency and then based on the concept of brainwave entrainment, produce binaural tunes at required slower frequencies, to help to relax and slowly move the brain activity spectrum down to delta waves, which ranges around 0.5-4 Hz. The feedback signals create a gradual shift in the received frequency, thereby entraining the mind to tend towards the desired slower brainwaves. The testing was performed on Jetson Nano and the resulting accuracy tested for RGB-based frequency classification was over 80% which led to a successful binaural tone generation to create a slow auditory output wave, thereby eventually increasing the delta activity in the brain. © 2022 IEEE.

关键词： Time series analysis

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Machine Learning Models for Cyberattack Detection in Industrial Control Systems

Machine Learning Models for Cyberattack Detection in Industr...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Arnold, David Ford, John Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

Industrial Control Systems (ICS) provide a network environment for operator command and control of cyber-physical systems and devices. As such, these systems are common throughout power plants, pipelines, manufactories, and other critical infrastructure. Due to the importance of critical infrastructure in everyday life, hackers of all stripes are taking an increased interest in compromising ICS for personal gain or nefarious purposes. Recent high-profile attacks also highlight the need to monitor system process and sensor data in detecting compromised user accounts or insider threats. Central to ICS data collection and monitoring, Data Historians hold the key to identifying malicious behavior and cyber breaches. Machine Learning techniques may be applied to detect data that is indicative of part failures or cyberattacks, allowing operators to take preventative measures prior to system failure. In this paper, several machine models for cyberattack detection within Industrial Control Systems will be introduced. These models will reside within the Data Historian and are implemented through the Apache Spark MLlib Libraries. The machine learning model were applied to an ICS Dataset to determine the system's accuracy at detecting cyberattacks. Naïve Bayes, Logistic Regression, Decision Tree Classifier, and Random Forest Classifier Machine Learning models were tested with the Tree Classifiers producing the most promising results. © 2022 IEEE.

关键词： Critical infrastructures

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Modular Delay Audio Effect System on FPGA

Modular Delay Audio Effect System on FPGA

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Cannon, Dean Fang, Tianyang Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper describes a System-on-Chip implementation of a Modular delay audio effect system. The Zedboard Zynq development board was used to implement the design. The programmable logic on the Zynq processor was used to handle the signal processing and the processing system was used to communicate between the programmable logic and software running on a computer to control different audio effect parameters in real-time. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Dynamic Pulsed Load Mitigation in PV-Battery-Supercapacitor Systems: A Hybrid PI-NN Controller Approach

Dynamic Pulsed Load Mitigation in PV-Battery-Supercapacitor ...

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2023 IEEE Design Methodologies Conference, DMC 2023

作者： Aghmadi, Ahmed Ali, Ola Hussein, Hossam Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350315547

DC microgrids are becoming more and more popular, however there are still difficulties involving ongoing instability that are mostly caused by imbalances in energy supply and demand, particularly in situations with pulse load or variable pulse load patterns. Innovative solutions are required since these disruptive load dynamics significantly decrease the effectiveness of conventional PV-Battery control systems. conventional Proportional-Integral (PI) controllers, although widely employed, may encounter limitations when confronted with the unique challenges posed by specific load patterns, notably pulse load scenarios. These dynamic load profiles, characterized by rapid and unpredictable changes. The inherent non-linearity and variability in such load conditions can result in suboptimal control performance, potentially leading to voltage instability and transients within the microgrid. To overcome these issues and improve control performance, the introduction of Neural Network (NN) controllers promises to be a potential approach. NN controllers have a remarkable capacity to learn and adapt from data, making them particularly well-suited for situations characterized by non-linear and dynamic load behavior. In this study, a hybrid PI-NN (Proportional-Integral Neural Network) controller is introduced to reduce load effects, especially pulsed loads, in PV systems with batteries and supercapacitors. This method efficiently optimizes energy storage. For voltage control, it utilizes a PI system, which divides the reference current into low and high-frequency components for battery and supercapacitor control. For enhanced sensitivity and dynamic responsiveness, neural network control replaces conventional PIs. The performance of the hybrid PI-NN controller is evaluated in this work, with an emphasis on its potential to improve PV system efficiency in severe load circumstances. © 2023 IEEE.

关键词： Supercapacitor

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