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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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Mini-InternVL: a flexible-transfer pocket multi-modal model with 5% parameters and 90% performance

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Visual Intelligence 2024年第1期2卷 1-17页

作者： Gao, Zhangwei Chen, Zhe Cui, Erfei Ren, Yiming Wang, Weiyun Zhu, Jinguo Tian, Hao Ye, Shenglong He, Junjun Zhu, Xizhou Lu, Lewei Lu, Tong Qiao, Yu Dai, Jifeng Wang, Wenhai Shanghai AI Laboratory Shanghai 200232 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science Nanjing University Nanjing 210023 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China School of Computer Science Fudan University Shanghai 200433 China SenseTime Research Shanghai 200233 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing 100084 China Department of Information Engineering The Chinese University of Hong Kong 999077 Hong Kong

Multi-modal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a wide range of domains. However, the large model scale and associated high computational cost pose significant challenges for training and deploying MLLMs on consumer-grade GPUs or edge devices, thereby hindering their widespread application. In this work, we introduce Mini-InternVL, a series of MLLMs with parameters ranging from 1 billion to 4 billion, which achieves 90% of the performance with only 5% of the parameters. This significant improvement in efficiency and effectiveness makes our models more accessible and applicable in various real-world scenarios. To further promote the adoption of our models, we are developing a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks, including autonomous driving, medical image processing, and remote sensing. We believe that our models can provide valuable insights and resources to advance the development of efficient and effective MLLMs. © The Author(s) 2024.

关键词： Knowledge distillation Lightweight multi-modal large language model Vision-language model Visual instruction tuning

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Dynamic Online Modulation Recognition using Incremental Learning

Dynamic Online Modulation Recognition using Incremental Lear...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Ali Owfi Ali Abbasi Fatemeh Afghah Jonathan Ashdown Kurt Turck Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA Computer Science Department Vanderbilt University Nashville TN USA Air Force Research Laboratory Rome NY USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Modulation recognition is a fundamental task in communication systems as the accurate identification of modulation schemes is essential for reliable signal processing, interference mitigation for coexistent communication technologies, and network optimization. Incorporating deep learning (DL) models into modulation recognition has demonstrated promising results in various scenarios. However, conventional DL models often fall short in online dynamic contexts, particularly in class incremental scenarios where new modulation schemes are encountered during online deployment. Retraining these models on all previously seen modulation schemes is not only time-consuming but may also not be feasible due to storage limitations. On the other hand, training solely on new modulation schemes often results in catastrophic forgetting of previously learned classes. This issue renders DL-based modulation recognition models inapplicable in real-world scenarios because the dynamic nature of communication systems necessitate the effective adaptability to new modulation schemes. This paper addresses this challenge by evaluating the performance of multiple Incremental Learning (IL) algorithms in dynamic modulation recognition scenarios, comparing them against conventional DL-based modulation recognition. Our results demonstrate that modulation recognition frameworks based on IL effectively prevent catastrophic forgetting, enabling models to perform robustly in dynamic scenarios.

关键词： Wireless communication Training Adaptation models Accuracy Heuristic algorithms Computational modeling Modulation

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Distributed and Rate-Adaptive Feature Compression

arXiv

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arXiv 2024年

作者： Deshmukh, Aditya Veeravalli, Venugopal V. Verma, Gunjan Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign ChampaignIL United States Army Research Laboratory AdelphiMD United States

We study the problem of distributed and rateadaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion center is assumed to contain a pretrained linear regression model, trained on a dataset of the entire uncompressed data. At inference time, the sensors compress their observations and send them to the fusion center through communication-constrained channels, whose rates can change with time. Our goal is to design a feature compression scheme that can adapt to the varying communication constraints, while maximizing the inference performance at the fusion center. We first obtain the form of optimal quantizers assuming knowledge of underlying regressor data distribution. Under a practically reasonable approximation, we then propose a distributed compression scheme which works by quantizing a one-dimensional projection of the sensor *** also propose a simple adaptive scheme for handling changes in communication constraints. We demonstrate the effectiveness of the distributed adaptive compression scheme through simulated experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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A lightweight high-voltage boost circuit for soft-actuated micro-aerial-robots

A lightweight high-voltage boost circuit for soft-actuated m...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhijian Ren Jiahui Yang Suhan Kim Yi-Hsuan Hsiao Jeffrey Lang Yufeng Chen Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology (MIT) Cambridge MA USA

Flight is an energetically expensive task. While aerial insects can effortlessly fly through natural environments, achieving power autonomous flights in insect-scale robots remains a major challenge. In prior works, we developed soft-actuated insect-scale aerial robots that demonstrated unique capabilities such as in-flight collision recovery and somersaults. However, the soft dielectric elastomer actuators (DEAs) have low efficiency (< 20%) and require a high driving voltage (>600 V). These properties represent formidable obstacles for soft aerial robots to achieve power autonomous flights. In this work, we developed a 127 mg boost circuit that can convert a 7.7 V DC input into a 600 V and 400 Hz output for driving a 120 mg DEA. It has an equivalent capacitance and resistance of 20 nF and 5 $\mathbf{k}\Omega$ , respectively. The DEA is assembled into a 158 mg aerial robot, which can demonstrate liftoff while carrying the boost circuit as a payload. Although the robot remains tethered to an off-board power supply, this result represents a first step towards achieving power autonomy in soft aerial robots.

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Smart Multi-Building Energy Monitoring System

Smart Multi-Building Energy Monitoring System

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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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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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Speed-Optimized Implementation of Fast Chirplet Decomposition Algorithm on FPGA-SoC

Speed-Optimized Implementation of Fast Chirplet Decompositio...

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IEEE International Conference on Electro-Information Technology

作者： Austin Fite Mikhail Gromov Tianyang Fang Jafar Saniie Department of Electrical and Computer Engineering ECASP Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

In ultrasonic nondestructive evaluation (NDE) of materials an essential step in characterizing an ultrasonic signal is decomposing the patterns of multiple interfering echoes. The Chirplet Transform (CT) is a powerful method to analyze the echoes in an ultrasonic signal. However, CT analysis is computationally heavy and impractical. Motivated by achieving real-time execution of the CT this research presents a speed-optimized implementation of the chirplet functions on FPGA. Chirplet echo generation used in Fast Chirplet Decomposition (FCD) Algorithm for ultrasonic signal analysis necessitates the frequent generation of chirplet functions with a 6-degree of freedom associated with chirplet parameters including the amplitude scaler; the time of arrival; the Gaussian envelope scaler; the phase of the chirplet; the center frequency and the frequency sweep. By minimizing the processing time of the chirplet generation, the FCD algorithm can be implemented efficiently on FPGA System-on-Chip (SoC). This study presents the hardware realization of the chirplet function on FPGA which is 37 times faster compared to using a Teensy 4.0 microcontroller, and 146 times faster than a highly popular Raspberry Pi 4.0 single board computer.

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Quantum Hardware Roofline: Evaluating the Impact of Gate Expressivity on Quantum Processor Design

arXiv

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arXiv 2024年

作者： Kalloor, Justin Weiden, Mathias Younis, Ed Kubiatowicz, John De Jong, Bert Iancu, Costin Department of Electrical Engineering and Computer Science University of California Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". This paper explores hardware design trade-offs across NISQ systems to guide algorithm and hardware design choices. The evaluation is driven by algorithmic workloads and algorithm fidelity models which capture architectural features such as gate expressivity, fidelity, and crosstalk. We also argue that the criteria for gate design and selection should be extended from maximizing average fidelity to a more comprehensive approach that takes into account the gate expressivity with respect to algorithmic structures. We consider native entangling gates (CNOT, ECR, CZ, ZZ, XX, Sycamore, √iSWAP), proposed gates (B Gate, √4 CNOT, √8 CNOT), as well as parameterized gates (FSim, XY). Our methodology is driven by a custom synthesis driven circuit compilation workflow, which is able to produce minimal circuit representations for a given system configuration. By providing a method to evaluate the suitability of algorithms for hardware platforms, this work emphasizes the importance of hardware-software co-design for quantum computing. © 2024, CC BY.

关键词： Economic and social effects

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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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