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- Single measurement vector (SMV) (4)
- Sparse Bayesian learning (SBL) (4)
- Asymptotic stability (2)
- Compressed sensing (CS) (2)
- Compressive sensing (2)
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- Eigenvalue estimation (2)
- Gerschgorin theorem (2)
- LTI systems (2)
- Modeling (2)
- Multiple measurement vectors (MMVs) (2)
- Uncertainty (2)
- Adaptive sampling (1)
- Approximate message passing (AMP) (1)
- Bayesian (1)
- Biological systems (1)
- Cluster structured sparsity (1)
- Clustered pattern (1)
- Clustered sparsity (1)
- Data refinement (1)
- Decision under conflict (1)
- Eigenvalues estimation (1)
- Energy efficiency (1)
- Epistemic utility controller (1)
- Erroneous support aided (1)
- Exploration (1)
- GPR (1)
- Gaussian process regression (1)
- Gaussian process regression (GPR) (1)
- Gaussian processes (1)
- Inference (1)
Articles 1 - 14 of 14
Full-Text Articles in Engineering
Stamina: Stochastic Approximate Model-Checker For Infinite-State Analysis, Thackur Neupane, Chris J. Myers, Curtis Madsen, Hao Zheng, Zhen Zhang
Stamina: Stochastic Approximate Model-Checker For Infinite-State Analysis, Thackur Neupane, Chris J. Myers, Curtis Madsen, Hao Zheng, Zhen Zhang
Electrical and Computer Engineering Faculty Publications
Stochastic model checking is a technique for analyzing systems that possess probabilistic characteristics. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. This paper presents a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing stochastic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. Demonstration of the tool on several benchmark examples shows promising …
Approximation Techniques For Stochastic Analysis Of Biological Systems, Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng, Chris J. Myers
Approximation Techniques For Stochastic Analysis Of Biological Systems, Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng, Chris J. Myers
Electrical and Computer Engineering Faculty Publications
There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to scale limits its applicability in practice. This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking. The proposed method is evaluated on a design of a genetic toggle switch. Comparisons with another state-of-the-art …
Exploration Vs. Data Refinement Via Multiple Mobile Sensors, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Exploration Vs. Data Refinement Via Multiple Mobile Sensors, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we …
Energy Efficient Network-On-Chip Architectures For Many-Core Near-Threshold Computing System, Chidhambaranathan Rajamanikkam, Jayashankara S. Rajesh, Koushik Chakraborty, Meher Samineni
Energy Efficient Network-On-Chip Architectures For Many-Core Near-Threshold Computing System, Chidhambaranathan Rajamanikkam, Jayashankara S. Rajesh, Koushik Chakraborty, Meher Samineni
Electrical and Computer Engineering Faculty Publications
Near threshold computing has unraveled a promising design space for energy efficient computing. However, it is still plagued by sub-optimal system performance. Application characteristics and hardware non-idealities of conventional architectures (those optimized for nominal voltage) prevent us from fully leveraging the potential of NTC systems. Increasing the computational core count still forms the bedrock of a multitude of contemporary works that address the problem of performance degradation in NTC systems. However, these works do not categorically address the shortcomings of the conventional on-chip interconnect fabric in a many core environment. In this work, we quantitatively demonstrate the performance bottleneck created …
Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report provides details on CSA-SBL(VB) algorithm for the recovery of sparse signals with unknown clustering pattern. More specifically, we deal with the recovery of sparse signals with unknown clustering pattern in the case of having partial erroneous prior knowledge on the supports of the signal. In [1], we provided a modified sparse Bayesian learning model to incorporate prior knowledge and simultaneously learn the unknown clustering pattern. For this purpose, we added one more layer to support-aided sparse Bayesian learning algorithm (SA-SBL) that was proposed in [2]. This layer adds a prior on the shape parameters of Gamma distributions, those …
Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the …
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report provides details on O-SBL(MCMC) algorithm for the recovery of jointly-sparse signals for the multiple measurement vector (MMV) problem. For the MMVs with this structure, the solution matrix, which is a collection of sparse vectors, is expected to exhibit joint sparsity across the columns. The notion of joint sparsity here means that the columns of the solution matrix share common supports. This algorithm employs a sparse Bayesian learning (SBL) model to encourage the joint sparsity structure across the columns of the solution. While the proposed algorithm is constructed for the MMV problems, it can also be applied to the …
A Note On Bayesian Linear Regression, Mohammad Shekaramiz, Todd K. Moon
A Note On Bayesian Linear Regression, Mohammad Shekaramiz, Todd K. Moon
Electrical and Computer Engineering Faculty Publications
In this report, we briefly discuss Bayesian linear regression as well as the proof for the inference to perform prediction based on the training data using this technique.
Details On Amp-B-Sbl: An Algorithm For Recovery Of Clustered Sparse Signals Using Approximate Message Passing [1-3], Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Amp-B-Sbl: An Algorithm For Recovery Of Clustered Sparse Signals Using Approximate Message Passing [1-3], Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
Solving the inverse problem of compressive sensing in the context of single measurement vector (SMV) problem with an unknown block-sparsity structure is considered. For this purpose, we propose a sparse Bayesian learning (SBL) algorithm simplified via the approximate message passing (AMP) framework. In order to encourage the block-sparsity structure, we incorporate the concept of total variation, called Sigma-Delta, as a measure of block-sparsity on the support set of the solution. The AMP framework reduces the computational load of the proposed SBL algorithm and as a result makes it faster compared to the message passing framework. Furthermore, in terms of the …
A Note On Kriging And Gaussian Processes, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
A Note On Kriging And Gaussian Processes, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
An introduction to gaussian processes and kriging.
Details On Gaussian Process Regression (Gpr) And Semi-Gpr Modeling, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Gaussian Process Regression (Gpr) And Semi-Gpr Modeling, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report tends to provide details on how to perform predictions using Gaussian process regression (GPR) modeling. In this case, we represent proofs for prediction using non-parametric GPR modeling for noise-free predictions as well as prediction using semi-parametric GPR for noisy observations.
On The Stability Analysis Of Perturbed Continuous T-S Fuzzy Models, Mohammad Shekaramiz, Farid Sheikholeslam
On The Stability Analysis Of Perturbed Continuous T-S Fuzzy Models, Mohammad Shekaramiz, Farid Sheikholeslam
Electrical and Computer Engineering Faculty Publications
This paper deals with the stability problem of continuous-time Takagi-Sugeno (T-S) fuzzy models. Based on the Tanaka and Sugeno theorem, a new systematic method is introduced to investigate the asymptotic stability of T-S models in case of having second-order and symmetric state matrices. This stability criterion has the merit that selection of the common positive-definite matrix P is independent of the sub-diagonal entries of the state matrices. It means for a set of fuzzy models having the same main diagonal state matrices, it suffices to apply the method once. Furthermore, the method can be applied to T-S models having certain …
On The Stability Analysis Of Linear Continuous-Time Distributed Systems, Mohammad Shekaramiz
On The Stability Analysis Of Linear Continuous-Time Distributed Systems, Mohammad Shekaramiz
Electrical and Computer Engineering Faculty Publications
This paper discusses the stability problem of linear continuous-time distributed systems. When dealing with large-scale systems, usually there is not thorough knowledge of the interconnection models between different parts of the entire system. In this case, a useful stability analysis method should be able to deal with high dimensional systems accompanied with bounded uncertainties for its interconnections. In this paper, in order to formulate the stability criterion for large-scale systems, stability analysis of LTI systems is first considered. Based on the existing methods for estimating the spectra of square matrices, sufficient criteria are proposed to guarantee the asymptotic stability of …
Simple Stability Criteria For Uncertain Continuous-Time Linear Systems, Mohammad Shekaramiz
Simple Stability Criteria For Uncertain Continuous-Time Linear Systems, Mohammad Shekaramiz
Electrical and Computer Engineering Faculty Publications
This paper mainly deals with the stability problem of continuous-time linear systems having uncertainties. Instead of using the tradition types of Lyapunov functions, this paper provides a very different method to investigate the stability of such systems. Hence, it reduces the conservativeness of having structured uncertainties belonging to convex sets. Based on a famous theorem that specifies regions containing all the eigenvalues of a complex square matrix, sufficient criteria are proposed to guarantee the asymptotic stability of linear systems. The main merit of this method is in analyzing linear systems having uncertainties. Moreover, the proposed criteria can also be used …