Electrical and Computer Engineering Commons^™

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 16, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 20, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 34, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 76, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 11, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 13, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 14, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 15, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 17, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 18, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 23, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 26, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 28, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 30, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 35, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 36, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 37, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 77, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 77 Lidar, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 22, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 24, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Neurocore: A Gnn Approach To Configurable Ip Core Identification In Fpga Netlists, Dallin Dahl, Keenan Faulkner, James Usevitch, Jeffrey Goeders

Student Works

Netlist reverse engineering enables many applications, including detecting IP theft, verifying CAD tool correctness, and detecting hardware trojans. However, reconstructing high-level information and circuit structure from a flat, nameless netlist is challenging. In this work we focus on the problem of locating known IP cores in an FPGA netlist, which is especially challenging due to the prevalence of highly configurable IP cores. We present Neurocore: a graph neural network-based approach to classifying nodes in a netlist as instances of known IP cores, and present and evaluate different models for different use cases. We have created a large open-source dataset of …

Silent Sabotage: Internal State Triggered Backdoor Attacks On Llm-Powered Robotic Systems, Doniyorkhon Obidov, Shivayogi Akki, Tan Chen, Kaichen Yang

Michigan Tech Publications

The integration of Large Language Models (LLMs) into robotic control systems is enabling a new generation of autonomous agents capable of complex reasoning and planning. While this paradigm shift accelerates progress, it also introduces novel security risks that remain largely unexplored. Current research into LLM backdoors has focused on attacks triggered by external stimuli, such as specific words, visual objects, or environmental states. These attacks, while potent, overlook a more insidious class of vulnerability where the trigger is internal to the agent’s own operational logic. This paper presents the first comprehensive study of history-based backdoor attacks on LLM-powered robotic systems. …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

The Impact Of A Structured Training Program On The Depth Perception Of Ab Initio Drone Pilots, John Murray, Steven Richardson, Keith Joiner, Graham Wild

Research outputs 2022 to 2026

Highlights: What are the main findings? First empirical assessment of depth-perception improvement from an official RPAS training program. Demonstrates measurable perceptual-skill gains despite no explicit syllabus objective for depth perception. What are the implications of the main findings? Conducted in authentic operational settings under regulated conditions, ensuring strong external validity. Establishes a validated baseline for future comparative and longitudinal studies on perceptual training effectiveness. Flying remotely requires accurate perception of the environment to ensure safe operation. While remotely piloted aircraft (RPA) bring unique opportunities, they also present new challenges for the pilot, including exercising accurate depth perception. The impact of …

Optical Fiber Sensors Using Vernier Effect In Cascaded Fiber Interferometers, Zhouchen Wang

Dissertations

The optical Vernier effect has emerged as a powerful tool to enhance the sensitivity of optical fiber interferometer-based sensors, opening new opportunities for developing highly sensitive fiber sensing systems. Optical fiber interferometric sensors based on the Vernier effect are widely used for various applications due to their ultra-compact size, high sensitivity, immunity to electromagnetic interference, electrical isolation, resistance to harsh environments, flexibility, multiplexing capability, and remote operation. The aim of this doctoral thesis was to gain a deeper fundamental understanding of the Vernier effect in optical fiber structures and to develop and investigate a series of novel Vernier effect optical …

The Role Of Spatial Abilities In Stem Learning And The Influence Of Individual Differences, Styliani Malkogeorgou

Masters

Students’ decisions to pursue education and careers in Science, Technology, Engineering, and Mathematics (STEM) are shaped by an interplay of cognitive, social, and motivational factors. Spatial ability is among the most reliable predictors of STEM success, yet less is known about how it relates to students’ STEM attitudes and aspirations, and whether visuospatial working memory (VSWM) explains this relationship. This study tested the hypotheses that (a) stronger spatial abilities and VSWM would be associated with more positive STEM attitudes and stronger STEM aspirations, and (b) VSWM would mediate the relationship between spatial abilities and STEM attitudes/aspirations, while examining the influence …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Roy Kravitz, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Julio Villalba-Moreno, Alexander Grinshpun, Freddy Gabbay, Luke Seed, Rui Duarte, Manuel Lopez, Oscar Alonso, Robert Owen

Electrical & Computer Engineering Faculty Research

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …