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.
Browse
Search Results
Item Dynamic ElGamal Public Key Generation with Tight Binding(1999) Poovendran, R.; Corson, M. Scott; Baras, John S.; ISR; CSHCNWe present a new distributed, group ElGamal public key generation algorithm which also permits maintenance of a group-specific, dynamic,individual ElGamal public key infrastructure.We parameterize the group with a time-varying quantity that servesas a distributed mechanism for controlling key generation privilege.
Our scheme can be viewed as an alternative to polynomial schemes where, at the time of the secret construction step, there has to be a third party or a black box to combine the shares. Also, in polynomial schemes, at the time of combining, the individual shares of the secret have to be revealed to the third party. In our scheme, the common secret can be generated without ever exposing the individual shares constructing it.
We note that many of the recently proposed distributed key management~[2-4] schemes need such group keys for certification and signing purposes.
Item VLSI Implemented ML Joint Carrier Phase and Timing Offsets Joint Estimator for QPSK/QQPSK Burst Modems(1999) Jiang, Yimin; Verahrami, F.B.; Richmond, R.L.; Baras, John S.; Baras, John S.; ISR; CSHCNA high performance ASIC supporting multiple modulation, error correction, and frame formats is under development at Hughes Network Systems, Inc. Powerful and generic data-aided (DA) estimators are needed to accommodate operation in the required modes. In this paper, a simplified DA maximum likelihood (ML) joint estimator for carrier phase and symbol timing offset for QPSK/OQPSK burst modems and a sample systolic VLSI implementation for the estimator are presented.Furthermore, the Cramer-Rao lower bound (CRLB) for DA case is investigated. The performance of the estimator is shown through simulation to meet the CRLB even at low signal-to-noise ratios (SNR). Compared with theoretical solutions, the proposed estimator is less computationally intensive and is therefore easier to implement using current VLSI technology. IEEE Wireless Communications and Networking Conference: WCNC'99
Item Optimal Multilevel Feedback Policies for ABR Flow Control using Two Timescale SPSA(1999) Bhatnagar, Shalabh; Fu, Michael C.; Marcus, Steven I.; ISROptimal multilevel feedback control policies for rate based flow controlin available bit rate (ABR) service in asynchronous transfer mode (ATM)networks are obtained in the presence of information and propagationdelays, using a numerically efficient two timescale simultaneousperturbation stochastic approximation (SPSA) algorithm. Convergenceanalysis of the algorithm is presented. Numerical experiments demonstratefast convergence even in the presence of significant delays and largenumber of parametrized policy levels.Item On the Use of Integer Programming Models in AI Planning(1999) Vossen, Thomas; Ball, Michael O.; Lotem, Amnon; Nau, Dana; ISRRecent research has shown the promise of using propositional reasoning and search to solve AI planning problems. In this paper, we further explore this area by applying Integer Programming to solve AI planning problems. The application of Integer Programming to AI planning has a potentially significant advantage, as it allows quite naturally for the incorporation of numerical constraints and objectives into the planning domain. Moreover, the application of Integer Programming to AI planning addresses one of the challenges in propositional reasoning posed by Kautz and Selman, who conjectured that the principal technique used to solve Integer Programs---the linear programming (LP) relaxation---is not useful when applied to propositional search. We discuss various IP formulations for the class of planning problems based on the STRIPS paradigm. Our main objective is to show that a carefully chosen IP formulation significantly improves the "strength" of the LP relaxation, and that the resultant LPs are useful in solving the IP and the associated planning problems. Our results clearly show the importance of choosing the "right" representation, and more generally the promise of using Integer Programming techniques in the AI planning domain.