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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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.
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.
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.
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
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
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.
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.
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.
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.
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.
Bat-Inspired Robot Navigation
(2009-08) Kuhlman, Michael Joseph; McRoberts, Kate; Horiuchi, Timothy K.; Krishnaprasad, P. S.
A key objective of Robotics is the autonomous navigation of mobile robots through an obstacle field. Inspired by echolocating bats, we developed a two-part navigation system consisting of obstacle detection through echolocation and motion planning. The first part relies upon a binaural sonar system, which emits ultrasonic pulses and then determines the interaural level difference (ILD) of the returning echoes to infer obstacle locations. Next, the Openspace motion planner computes the best direction of travel based on the locations of the target and the detected obstacles. We implemented this navigation system on a mobile platform, which repeatedly computes the safest direction of travel and moves accordingly, ultimately generating a real-time path to the goal.
Optimal replacement strategy for residential solar panels using monte carlo simulations and nonlinear optimization methods
(2017-05) Poissant, Andrew; Adomaitis, Raymond
The purpose of this analysis is to determine the optimal replacement strategy for a residential photovoltaic (PV) array. Specifically, the optimal year and number of solar modules that should be replaced on a residential solar panel system. This analysis aims at saving the stakeholder, a homeowner with a residential PV array, money. A Monte Carlo simulation and nonlinear mixed-integer programming are the analytic techniques used in determining the replacement strategy. Localized cost of electricity (LCOE) is the objective function in these analyses. Modular, environmental, and market factors are all variables that can affect the LCOE. University of Maryland’s LEAFHouse was the basis of these analyses because it is a house equipped with an aging PV array and readily accessible data. Based on the findings in this report, it was determined that 0 ± 0 solar modules should be replaced after 1.42 ± 0.32 years with a reference year of initial installation being 2007. While the analysis results were not expected, they were proven to be reasonable based on cost trends for solar panels and the calculated monetary value of the power production lost from the PV array.
On The Number of Unlabeled Bipartite Graphs
(2016) Atmaca, Abdullah; Oruc, Yavuz A
Let $I$ and $O$ denote two sets of vertices, where $I\cap O =\Phi$, $|I| = n$, $|O| = r$, and $B_u(n,r)$ denote the set of unlabeled graphs whose edges connect vertices in $I$ and $O$. It is shown that the following two-sided equality holds. $\displaystyle \frac{\binom{r+2^{n}-1}{r}}{n!} \le |B_u(n,r)| \le 2\frac{\binom{r+2^{n}-1}{r}}{n!} $
A Framework for Design Theory and Methodology Research
(2016-04) Herrmann, Jeffrey
The scholarly study of design continues to develop new knowledge through a variety of approaches. Some researchers examine how designers work, and many develop new methods to help designers do design tasks. Studying design is complex for many reasons. There are many domains in which design occurs, including all of the disciplines of engineering, architecture, and other fields. More significantly, humans design, and human behavior can be difficult to understand. Designers sometimes work alone and sometimes in a group or team. Designers experience design work in multiple ways. Design researchers have been exploring many different aspects of design and experimenting with many different approaches and generating a variety of different design theories. The focus on exploration, however, has meant that there has been less emphasis on exploiting previous research and creating an organized body of knowledge. Building a unified body of knowledge is a long-term challenge. This paper describes a proposed framework for design theory and methodology research. This framework, which is based on ideas from education research, does not specify specific topics or methodologies. Instead, it describes six different research types: (1) Foundational Research, (2) Early-Stage or Exploratory Research, (3) Design and Development Research, (4) Efficacy Research, (5) Effectiveness Research, and (6) Scale-up Research. Illustrating these types are examples based on a table design example. The paper explains how these six research types are related to each other and how, collectively, they serve to generate valid knowledge about design. The research types follow a logical sequence in which researchers develop basic knowledge, create design methods, and test design methods. Although the framework numbers the research types following this natural progression, it does not insist that researchers do or should work by rigidly following this sequence. These research types actually form a cycle of research that iterates through three “phases”: description, explanation, and testing. In this cycle, researchers observe and describe a phenomenon, develop theories to explain the phenomenon and its interactions and effects, and test that theory against the phenomenon, and then, based on the results, refine their descriptions, revise their theories, and conduct more testing. Over time, the description of the phenomenon is improved (e.g., made more precise or more general), better explanations (theories) are found, and additional testing further demonstrates their correctness (or indicates their limitations). The proposed framework can show how different research studies are related to each other because they are the same research type or they fit into the progress of a design theory or the development of a design method. Thus, the proposed framework, while not a theory of design, can help researchers respond to the challenges of coordinating the different types of research needed to create useful design theories and build a unified body of knowledge. Future work is needed to analyze, test, and refine this framework so that it becomes truly useful to the design research community.
Physically Constrained Design Space Modeling for 3D CPUs
(2015-12) Serafy, Caleb; Srivastava, Ankur; Yeung, Donald; Srivastava, Ankur; Yeung, Donald
Design space exploration (DSE) is becoming increasingly complex as the number of tunable design parameters increases in cutting edge CPU designs. The advent of 3D integration compounds the problem by expanding the architectural design space, causing intricate links between memory and logic behavior and increasing the interdependence between physical and architectural design. Exhaustive simulation of an architectural design space has become computationally infeasible, and previous work has proposed fast DSE methodologies using modeling or pseudo-simulation. Modeling techniques can be used to predict design space properties by regression fitting. However in the past such techniques have only been applied to optimization metrics such as performance or energy efficiency while physical constraints have been ignored. We propose a technique to apply spline modeling on a 3D CPU design space to predict optimization metrics and physical design properties (e.g. power, area and temperature). We use these models to identify optimal 3D CPU architectures subject to physical constraints while drastically reducing simulation time compared to exhaustive simulation. We show that our technique is able to identify design points within 0.5% of the global optimal while simulating less than 5% of the design space.
Digital Signal Processors: A brief summary
(2008) Kotha, Aparna; Barua, Rajeev
Ambiguous Behavior of Logic Bistable Systems
(1975-10-04) Hurtado, Marco; Elliott, David L.
The standard specification of logic bistable devices do not specify the behavior under conditions in which the input is logically undefined or in which certain kinds of multiple input changes occur. These conditions are unavoidable in logic synchronizers and arbiters. A general deterministic model of bistable devices is proposed, consisting of a non-liner differential system with some adequate properties. Analysis of this model shows that bistable devices can be driven into a logically undefined region by certain admissible inputs and can remain in this region for an unbounded length of time.
Instances for the Generalized Regenerator Location Problem
(2015) Chen, Si; Ljubic, Ivana; Raghavan, S.
A GENERAL FRAMEWORK FOR CONSENSUS NETWORKS
(2015-03-10) Somarakis, Christoforos; Baras, John
A new framework for the analysis of consensus networks is developed. The theory consists of necessary and sufficient conditions and it is flexible enough to comprise a variety of consensus systems. Under mild connectivity assumptions, the discussion ranges from linear, nonlinear, ordinary, functional and leader-follower models. The establishment of explicit estimates on the rate of convergence is the central objective. Our work extends and unifies past related works in the literature. Illustrative examples and simulations are presented to outline the theoretical results.
Nonlinear Programming Methods for Distributed Optimization
(2015-01) Matei, Ion; Baras, John
In this paper we investigate how standard nonlinear programming algorithms can be used to solve constrained optimization problems in a distributed manner. The optimization setup consists of a set of agents interacting through a communication graph that have as common goal the minimization of a function expressed as a sum of (possibly non-convex) differentiable functions. Each function in the sum corresponds to an agent and each agent has associated an equality constraint. By re-casting the distributed optimization problem into an equivalent, augmented centralized problem, we show that distributed algorithms result naturally from applying standard nonlinear programming tech- niques. Due to the distributed formulation, the standard assumptions and convergence results no longer hold. We emphasize what changes are necessary for convergence to still be achieved for three algorithms: two algorithms based on Lagrangian methods, and an algorithm based the method of multipliers. The changes in the convergence results are necessary mainly due to the fact that the (local) minimizers of the lifted optimization problem are not regular, as a results of the distributed formulation. Unlike the standard algorithm based on the method of multipliers, for the distributed version we cannot show that the theoretical super-linear convergence rate can be achieved.

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