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IEEE International Conference on Electro-Information Technology

作者： Saurabh Saluja Colin Prochnow Grant Couper Filip Zivko Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents an intelligent system for monitoring energy usage in smart buildings. It functions as a bridge between the smart grid and smart buildings. A web-based platform is designed to monitor sensor data and track power consumption on the grid. AI technology provides tips for energy conservation based on factors such as power usage patterns and weather conditions. The adaptability of our approach allows for the tuning of automated protocols based on evolving power usage patterns and grid conditions. Data is transmitted between the client devices and the central server via a Zigbee network organized in a star formation, allowing for mutual communication. Our implementation results in the improvement of the overall energy efficiency of supported infrastructure while ensuring continuous and optimized operation in case of a bulk grid blackout.

关键词： Smart buildings Energy conservation Zigbee Stars Smart homes Energy efficiency Smart grids

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Advanced Robotic Surveillance for Urban Air Quality Safety

Advanced Robotic Surveillance for Urban Air Quality Safety

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IEEE International Conference on Electro-Information Technology

作者： Guillaume Hansen James R. Benson Muhammad Rafay Danish Khoa Truong Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents a multifunctional quadruped robot, specifically engineered for comprehensive air quality monitoring and emergency assistance. Designed to navigate through urban environments, the robot autonomously collects and transmits real-time air quality data to a centralized database. This interdisciplinary paper integrates robotics, environmental science, and emergency response protocols to propose novel solutions for smart city infrastructure, public health, and disaster management. Specifically, this research highlights the critical role that autonomous systems play in monitoring environmental conditions and advancing safety protocols. This research leverages a comprehensive array of sensors including CO2 (MH-Z19), particle (SDS 011), GPS (ADA 746), DHT 22, MQ 9, and ADS 1115 converter, each meticulously implemented to ensure precise environmental data collection and analysis. Furthermore, the integration of cutting-edge technologies such as Robot Operating System (ROS), .NET MAUI, Python, and MariaDB establishes a robust framework for seamless operation, data processing, and secure storage within the quadruped robot system.

关键词： Temperature sensors Protocols Robot sensing systems Dynamic scheduling Sensors Quadrupedal robots Servers

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Basketball Video Analysis for Automated Game Data Acquisition Deep Learning

Basketball Video Analysis for Automated Game Data Acquisitio...

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IEEE International Conference on Electro-Information Technology

作者： Diego Rodriguez Garcia Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

The utilization of data analytics to gain insights into the game of basketball has seen a remarkable surge in the past decade. Leagues such as the National Basketball Association are continuously exploring innovative methods to analyze game data, an approach that has significantly influenced the dynamics of the game. But to perform these analyses, a growing amount of data is needed, which is traditionally annotated by humans. This work proposes a 3-stage system able to automatically acquire relevant basketball game data from a broadcast video. The first stage is an object detector combined with a tracking algorithm to extract the main elements present in a basketball game video. Then, the players' visual information is analyzed to identify the players based on pixel color analysis and number recognition. Finally, a statistics generation algorithm assigns the game events to the corresponding player and team, so that the system can be used as an aid for box score annotation in major leagues, low-cost annotation in amateur games, or in-depth game video analysis.

关键词： Visualization Annotations Image color analysis Data acquisition Optical character recognition Games Detectors

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Combining Relative and Absolute Learning Formulations to Predict Emotional Attributes From Speech

Combining Relative and Absolute Learning Formulations to Pre...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Abinay Reddy Naini Shruthi Subramanium Seong-Gyun Leem Carlos Busso Department of Electrical and Computer Engineering Multimodal Signal Processing (MSP) Laboratory The University of Texas at Dallas Richardson TX USA

Predicting absolute scores is the most common speech-emotion recognition (SER) task when predicting emotional attributes (i.e., valence, arousal, and dominance). However, studies have shown that emotion has an ordinal nature where it is more reliable to establish a preference between speech samples (e.g., one sample is more positive than the other). This paper pursues a novel direction to combine absolute and relative learning formulations for SER. The proposed multitask formulation can simultaneously estimate preference between speech samples and predict their absolute score, providing a flexible tool to analyze emotional content in speech. Both tasks mutually complement each other, allowing the model to outperform SER systems that are exclusively trained to either predict absolute scores or estimate preferences. The multitask weights can be set according to the intended applications, prioritizing one task while slightly compromising the performance of the other task.

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Variation Bayesian Interference for Multiple Extended Targets or Unresolved Group Targets Tracking

arXiv

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arXiv 2024年

作者： Cheng, Yuanhao Cao, Yunhe Yeo, Tat-Soon Zhang, Yulin Jie, Fu National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

In this work, we propose a method for tracking multiple extended targets or unresolvable group targets in clutter environment. Firstly, based on the Random Matrix Model (RMM), the joint state of the target is modeled as the Gamma Gaussian Inverse Wishart (GGIW) distribution. Considering the uncertainty of measurement origin caused by the clutters, we adopt the idea of probabilistic data association and describe the joint association event as an unknown parameter in the joint prior distribution. Then the Variational Bayesian Inference (VBI) is employed to approximately solve the non-analytical posterior distribution. Furthermore, to ensure the practicability of the proposed method, we further provide two potential lightweight schemes to reduce its computational complexity. One of them is based on clustering, which effectively prunes the joint association events. The other is a simplification of the variational posterior through marginal association probabilities. Finally, the effectiveness of the proposed method is demonstrated by simulation and real data experiments, and we show that the proposed method outperforms current state-of-the-art methods in terms of accuracy and adaptability. Copyright © 2024, The Authors. All rights reserved.

关键词： Target tracking

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Facial Recognition Attendance Tracking: An Intelligent Monitoring Approach

Facial Recognition Attendance Tracking: An Intelligent Monit...

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IEEE International Conference on Electro-Information Technology

作者： Syed Tauseeq Hussain Mario Antonio Nogueira Mansur Carvalho Vikrant Kishor Rathod Srinivasa Mani Sankar Reddy Karri Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents an Intelligent Monitoring System that utilizes the Internet of Things (IoT) and Artificial Intelligence (AI) technologies to automate the class attendance process reliably and efficiently. Conventional approaches for attendance tracking have been laborious and have consumed a significant amount of time, with errors and inconsistencies being a common occurrence. In contrast, the Intelligent Monitoring System combines object detection & recognition AI models, wireless communication, and cloud monitoring to generate reliable attendance data that can be used for various purposes, such as tracking student-by-student attendance data and monitoring overall attendance statistics. The system comprises an ID reader that uses radio frequency tags, a facial recognition system that uses a camera and AI algorithms, and a cloud monitoring system for attendance statistics. The proposed system is designed to overcome the challenges of traditional attendance-taking processes and provide a solution that is accurate, reliable, and efficient.

关键词： Wireless communication Cloud computing Face recognition Scalability Authentication Reliability engineering Security

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Implementation of a Robust Chirplet signal Decomposition Algorithm on FPGA-SoC Platform for Ultrasonic Flaw Detection

Implementation of a Robust Chirplet Signal Decomposition Alg...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Tianyang Fang Austin Fite Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

This research presents a robust chirplet decomposition algorithm designed for hardware implementation, specifically aimed at enhancing ultrasonic nondestructive testing and imaging. The study focuses on developing an algorithm and an FPGA hardware module that enable real-time chirplet decomposition, emphasizing increased processing speed while maintaining echo accuracy. A novel Maximum Likelihood Estimation (MLE)-based parameter estimation algorithm was developed to improve chirplet parameter estimation, enhancing both accuracy and convergence speed. This high-performance platform enables real-time ultrasonic imaging for flaw detection in steel specimens, demonstrating significant improvements in defect detection and characterization. The resultant platform has been tested against four alternative platforms to evaluate its effectiveness in enhancing execution times. Remarkably, when compared to similar embedded platforms, this speed-optimized platform achieves a 150-fold increase in processing speed, surpassing the next fastest embedded platform, the Teensy 4.0, which includes a Cortex-M7 processor operating at 600MHz. Additionally, when compared to an AMD Ryzen 7 3700X, this platform operates 6.6 times faster.

关键词： Parameter estimation Accuracy Ultrasonic imaging Chirp signal processing algorithms Transforms Hardware Acoustics Real-time systems Steel

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AI Smart Security Camera for Person Detection, Face Recognition, Tracking and Logging

AI Smart Security Camera for Person Detection, Face Recognit...

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IEEE International Conference on Electro-Information Technology

作者： Diego Rodriguez Garcia Alvaro Aparicio Serna David Sancho Crespo Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Although the surveillance industry is growing due to increased concerns for public safety, traditional surveillance systems can be ineffective due to fixed positions, limited coverage, and the need for human operation. This paper proposes an edge computing surveillance system that can detect, track, and identify human presence using AI software. The system includes a video camera integrated into a structure that allows pan and tilt movements and a processing unit that performs all computations locally. The system can operate in two different modes, manual and automatic, and it can be controlled from a user-friendly web application. The manual mode allows the user to control the camera remotely, adjusting the position with buttons and handling additional functionalities like zoom. On the other hand, in automatic mode, the system detects people and identifies if they are an unknown subject or not. Identifications are recorded in an online log and the system takes pictures of unknown people to recognize recurrent intruders.

关键词： Tracking Surveillance Face recognition Streaming media Cameras Software Real-time systems

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Autonomous Patrol and Threat Detection Through Integrated Mapping and computer Vision

Autonomous Patrol and Threat Detection Through Integrated Ma...

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IEEE International Conference on Electro-Information Technology

作者： Robert Soler Alae Moudni Gabriel Roskowski Xinrui Yu Mikhail Gormov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents the creation of an innovative autonomous security robot designed to perform security functions with efficiency and reliability. The robot boasts mapping capabilities, which it utilizes to facilitate autonomous patrol in designated areas. Its primary operations involve the use of computer vision to detect violence, identify weapons and dangerous items, and recognize individuals. Critical incidents are met with an immediate alarm and the subsequent transmission of data to a central security server, which then generates comprehensive reports displayed through a web application for security personnel. The application itself features remote control of the robot, incident report management, status updates, and incident analytics. The robot demonstrates substantial real-world application potential, particularly in crowded environments where it could outperform conventional surveillance. The project combines concepts of engineering, computer science, and cybersecurity, functioning per design but with considerable potential for future refinement and expansion, embodying the concept of an evolving technological solution.

关键词： computer science computer vision Technological innovation Surveillance Weapons Reliability engineering Threat assessment

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Learning Cross-Modal Audiovisual Representations with Ladder Networks for Emotion Recognition

Learning Cross-Modal Audiovisual Representations with Ladder...

引用

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

作者： Lucas Goncalves Carlos Busso Department of Electrical and Computer Engineering Multimodal Signal Processing (MSP) Laboratory The University of Texas at Dallas Richardson TX USA

Representation learning is a challenging, but essential task in audiovisual learning. A key challenge is to generate strong cross-modal representations while still capturing discriminative information contained in unimodal features. Properly capturing this information is important to increase accuracy and robustness in audiovisual tasks. Focusing on emotion recognition, this study proposes novel cross-modal ladder networks to capture modality-specific information while building strong cross-modal representations. Our method utilizes representations from a backbone network to implement unsupervised auxiliary tasks to reconstruct intermediate layer representations across the acoustic and visual networks. The skip connections between the cross-modal encoder and decoder provide powerful modality-specific and multimodal representations for emotion recognition. Our model on the CREMA-D corpus achieves high performance with precision, recall, and F1 scores over 80% on a six-class problem.

关键词： Representation learning Emotion recognition Visualization Focusing Speech recognition signal processing Acoustics

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