Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Now showing 1 - 10 of 2409
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    TimingCamouflage+ Decamouflaged
    (Association for Computer Machinery (ACM), 2023-06-05) Mittu, Priya; Liu, Yuntao; Srivastava, Ankur
    In today’s world, sending a chip design to a third party foundry for fabrication poses a serious threat to one’s intellectual property. To keep designs safe from adversaries, design obfuscation techniques have been developed to protect the IP details of the design. This paper explains how the previously considered secure algorithm, TimingCamouflage+, can be thwarted and the original circuit can be recovered [15]. By removing wave-pipelining false paths, the TimingCamouflage+ algorithm is reduced to the insecure TimingCamouflage algorithm [16]. Since the TimingCamouflage algorithm is vulnerable to the TimingSAT attack, this reduction proves that TimingCamouflage+ is also vulnerable to TimingSAT and not a secure camouflaging technique [7]. This paper describes how wave-pipelining paths can be removed, and this method of handling false paths is tested on various benchmarks and shown to be both functionally correct and feasible in complexity.
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    Using Metareasoning on a Mobile Ground Robot to Recover from Path Planning Failures
    (2023-02) Molnar, Sidney; Mueller, Matt; Macpherson, Robert; Rhoads, Lawrence; Herrmann, Jeffrey W.
    Autonomous mobile ground robots use global and local path planners to determine the routes that they should follow to achieve mission goals while avoiding obstacles. Although many path planners have been developed, no single one is best for all situations. This paper describes metareasoning approaches that enable a robot to select a new path planning algorithm when the current planning algorithm cannot find a feasible solution. We implemented the approaches within a ROS-based autonomy stack and conducted simulation experiments to evaluate their performance in multiple scenarios. The results show that these metareasoning approaches reduce the frequency of failures and reduce the time required to complete the mission.
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    Data for: Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions
    (2021) Brodbeck, Christian; Bhattasali, Shohini; Das, Proloy; Simon, Jonathan Z.
    This dataset accompanies “Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions” (Brodbeck et al., 2021) and is a derivative of the Alice EEG datasets collected at the University of Michigan Computational Neurolinguistics Lab (Bhattasali et al., 2020), licensed under CC BY (https://creativecommons.org/licenses/by/4.0/) and the original work can be found at DOI: 10.7302/Z29C6VNH. The files were converted from the original matlab format to fif format in order to be compatible with Eelbrain. This dataset includes the EEG data for 33 participants, which were used in the example analyses for the paper. The original Alice dataset included data from all 49 participants and participants were excluded due to artifacts and incorrect behavioral responses (for more details see Bhattasali et al., 2020). You can use the Python script data_grab.py at https://github.com/christianbrodbeck/Alice-Eelbrain to download and unzip these files into a specified destination folder.
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    Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise
    (PLOS Computational Biology, 2020-08-02) Presacco, Alessandro; Miran, Sina; Fu, Michael; Marcus, Steven; Simon, Jonathan; Babadi, Behtash
    MEG data used for the "Switching attention" experiment. This set of data refers to the part of the "forced" switching of attention
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    Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise
    (PLOS Computational Biology, 2020-08-02) Presacco, Alessandro; Miran, Sina; Fu, Michael; Marcus, Steven; Jonathan, Simon; Babadi, Betash
    MEG data used for the "Switching attention" experiment
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    Data-driven Metareasoning for Collaborative Autonomous Systems
    (2020-01) Herrmann, Jeffrey
    When coordinating their actions to accomplish a mission, the agents in a multi-agent system may use a collaboration algorithm to determine which agent performs which task. This paper describes a novel data-driven metareasoning approach that generates a metareasoning policy that the agents can use whenever they must collaborate to assign tasks. This metareasoning approach collects data about the performance of the algorithms at many decision points and uses this data to train a set of surrogate models that can estimate the expected performance of different algorithms. This yields a metareasoning policy that, based on the current state of the system, estimated the algorithms’ expected performance and chose the best one. For a ship protection scenario, computational results show that one version of the metareasoning policy performed as well as the best component algorithm but required less computational effort. The proposed data-driven metareasoning approach could be a promising tool for developing policies to control multi-agent autonomous systems.
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    BPOD: A WIRELESS INTEGRATED SENSOR PLATFORM FOR CONTINUOUS LOCALIZED BIOPROCESS MONITORING
    (2019) Stine, Justin; Ghodssi, Reza
    Process parameter spatial inhomogeneities inside cell culture bioreactors has attracted considerable attention, however, few technologies allow investigation of the impact of these variations on process yield. Commercially available sensing probes sit at fixed locations, failing to capture the spatial distribution of process metrics. The bio-Processing online device (bPod) addresses this problem by performing real-time in situ monitoring of dissolved oxygen (DO) within bioreactor cell cultures. The bPod is an integrated system comprised of a potentiostat analog-front-end, a Bluetooth Low Energy microcontroller, and a Clark-type electrochemical DO sensor. The Clark-type sensor uses chronoamperometry to determine the DO percent saturation within a range relevant for mammalian cell culture. The free-floating capsule is packaged inside a 3D-printed biocompatible shell and wirelessly transmits data to a smartphone while submerged in the reactor. Furthermore, the bPod demonstrated a sensitivity of 37.5 nA/DO%, and can be adapted to multiple sensor types, enabling numerous bioprocess monitoring applications.
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    Reaction path analysis for atomic layer deposition systems
    (FOCAPO/CPC 2017, 2017-01-08) Adomaitis, Raymond
    In this paper, we examine the mathematical structure of thin-film deposition process reaction kinetics models with the goal of determining whether a reaction network can guarantee the self-limiting and stable growth inherent in true atomic layer deposition systems. This analysis is based on identifying reaction invariants and interpreting the chemical significance of these conserved modes. A species-reaction graph approach is introduced to aid in distinguishing “proper” from problematic ALD reaction networks.
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    Dynamic dimension reduction for thin-film deposition reaction network models
    (IFAC, 2016-06-06) Adomaitis, Raymond
    A prototype thin-film deposition model is developed and subsequently used in a sequence of model reduction procedures, ultimately reducing the dynamic dimension from six to one with essentially no loss in accuracy to the dynamics of the deposition process. The species balance model consists of a singular perturbation problem of nonstandard form which first is numerically solved following the approach of Daoutidis (2015). An alternative strategy then is presented, consisting of a reaction factorization procedure which facilitates the solution of the outer solution of the singular perturbation problem and provides unique physical insight into the conserved quantities (reaction invariants) identified by the elimination of redundant dynamic modes. Further reduction in dynamic dimension then is achieved through a second factorization focused only on the major reaction species. This second reduction procedure identifies pseudo- equilibria of finite-rate properties and introduces an additional level of complexity to the challenges of identifying consistent initial conditions for DAE systems.
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    Framework for Knowledge-Based Fault Detection and Diagnostics in Multi-Domain Systems: Application to HVAC Systems
    (2017-11-13) Delgoshaei, Parastoo; Austin, Mark
    State-of-the-art fault detection methods are equipment and domain specific and non-comprehensive. As a result, the applicability of these methods in different domains is very limited and they can achieve significant levels of performance by having knowledge of the domain and the ability to mimic human thinking in identifying the source of a fault with a comprehensive knowledge of the system and its surroundings. This technical report presents a comprehensive semantic framework for fault detection and diagnostics (FDD) in systems simulation and control. Our proposed methodology entails of implementation of the knowledge bases for FDD purposes through the utilization of ontologies and offers improved functionalities of such system through inference-based reasoning to derive knowledge about the irregularities in the operation. We exercise the proposed approach by working step by step through the setup and solution of a fault detection and diagnostics problem for a small-scale heating, ventilating and air-conditioning (HVAC) system.