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Convergence of Expectation-Maximization Algorithm with Mixed-Integer Optimization

Joseph, G. (author)

The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables). The requirement is not met when parameters comprise both discrete and continuous variables, making the convergence analysis nontrivial. This paper...

journal article 2024

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Occupancy Grid Mapping for Automotive Driving Exploiting Clustered Sparsity

Önen, Çağan (author), Pandharipande, Ashish (author), Joseph, G. (author), Myers, N.J. (author)

Occupancy grid maps provide information about obstacles and available free space in the environment and are crucial in automotive driving applications. An occupancy map is constructed using point cloud data from sensor modalities such as light detection and ranging (LiDAR) and radar used for automotive perception. In this article, we...

journal article 2024

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Using data collection to build trust and ownership in transboundary water allocation planning: a case study from the Mara River Basin

Zielinski, Lauren (author), McClain, M.E. (author), Ojwang, William (author), Joseph, Christian (author), Tickner, David (author), Mumbo, Gordon (author), Matano, Ali Said (author), Nobert, Joel (author), Huber-Lee, Annette (author)

Following a 2015 Memorandum of Understanding, efforts began to develop a transboundary water allocation plan in the Mara River Basin between Kenya and Tanzania. Many lessons were learned along that way, including the importance of involving basin and national water authorities in all phases of data collection, planning and decision-making;...

journal article 2024

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LiDAR-Based Occupancy Grid Map Estimation Exploiting Spatial Sparsity

Önen, Çağan (author), Pandharipande, Ashish (author), Joseph, G. (author), Myers, N.J. (author)

The problem of estimating occupancy grids to support automotive driving applications using LiDAR sensor point clouds is considered. We formulate the problem as a sparse binary occupancy value reconstruction problem. Our proposed occupancy grid estimation method is based on pattern-coupled sparse Bayesian learning and exploits the inherent...

conference paper 2023

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Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing

Joseph, G. (author), Zhong, Chen (author), Gursoy, M. Cenk (author), Velipasalar, Senem (author), Varshney, Pramod (author)

We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that...

journal article 2023

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Output Controllability of a Linear Dynamical System with Sparse Controls

Joseph, G. (author)

In this article, we study the conditions to be satisfied by a discrete-time linear system to ensure output controllability using sparse control inputs. A set of necessary and sufficient conditions can be directly obtained by extending the Kalman rank test for output controllability. However, the verification of these conditions is...

journal article 2023

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Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO

He, Y. (author), Joseph, G. (author)

This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve...

conference paper 2023

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Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers

Bulusu, Saikiran (author), Joseph, G. (author), Gursoy, M. Cenk (author), Varshney, Pramod K. (author)

We consider a set of data samples such that a fraction of the samples are arbitrary outliers, and the rest are the output samples of a single-layer neural network with rectified linear unit (ReLU) activation. Our goal is to estimate the parameters (weight matrix and bias vector) of the neural network, assuming the bias vector to be non-negative....

conference paper 2022

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State Estimation of Linear Systems With Sparse Inputs and Markov-Modulated Missing Outputs

Joseph, G. (author), Varshney, Pramod K. (author)

In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the...

conference paper 2022

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Near-field focusing using phased arrays with dynamic polarization control

Myers, N.J. (author), Aslan, Y. (author), Joseph, G. (author)

Phased arrays in near-field communication allow the transmitter to focus wireless signals in a small region around the receiver. Proper focusing is achieved by carefully tuning the phase shifts and the polarization of the signals transmitted from the phased array. In this paper, we study the impact of polarization on near-field focusing and...

conference paper 2022

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Stabilizability of Linear Dynamical Systems Using Sparse Control Inputs

Sriram, Chandrasekhar (author), Joseph, G. (author), Murthy, Chandra R. (author)

The stabilizability of a linear dynamical system (LDS) refers to the existence of control inputs that drive the system state to zero. In this article, we analyze both the theoretical and algorithmic aspects of the stabilizability of an LDS using sparse control inputs with potentially time-varying supports. We show that an LDS is stabilizable...

journal article 2022

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Influence of mix design parameters on reactivity and setting of alkali-activated materials

Mutti, Marcello (author), Joseph, Shiju (author), Cizer, Özlem (author)

The mix design of alkali-activated materials (AAMs) is developed by taking into account several interdependent parameters. Therefore, the optimization of these to achieve a robust composition might represent a great challenge. The aim of this study is to investigate and compare the influence of some key parameters on the reactivity and setting...

conference paper 2021

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Robust evidence for the stabilization of the premartensite phase in Ni-Mn-In magnetic shape memory alloys by chemical pressure

Singh, Anupam K. (author), Singh, Sanjay (author), Dutta, B. (author), Dubey, K.K. (author), Joseph, Boby (author), Rawat, R. (author), Pandey, Dhananjai (author)

The thermodynamic stability of the premartensite (PM) phase has been a topic of extensive investigation in shape memory alloys as it affects the main martensite phase transition and the related physical properties. In general, the PM phase is stable over a rather narrow temperature-composition range. We present here evidence for chemical...

journal article 2021

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Comparing Cryo-EM Reconstructions and Validating Atomic Model Fit Using Difference Maps

Joseph, Agnel Praveen (author), Lagerstedt, Ingvar (author), Jakobi, A. (author), Burnley, Tom (author), Patwardhan, Ardan (author), Topf, Maya (author), Winn, Martyn (author)

Cryogenic electron microscopy (cryo-EM) is a powerful technique for determining structures of multiple conformational or compositional states of macromolecular assemblies involved in cellular processes. Recent technological developments have led to a leap in the resolution of many cryo-EM data sets, making atomic model building more common...

journal article 2020

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An Under Voltage Load Shedding Scheme Based on a Short Term Voltage Instability Detection Method

Joseph, A. (author), Cvetkovic, M. (author), Palensky, P. (author)

Faster detection of faults, can lead to better and more efficient control of power system against cascading failures triggered by the faults. This article presents a novel detection algorithm using statistical methods for the detection of events that lead to FIDVR. Furthermore we show that the algorithm is computationally efficient and quick...

conference paper 2019

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Grid Awareness Under Normal Conditions and Cyber-Threats

Naglic, M. (author), Joseph, A. (author), Pan, K. (author), Popov, M. (author), van der Meijden, M.A.M.M. (author), Palensky, P. (author)

The situational grid awareness is becoming increasingly important for power system operations due to smaller operational margins, wide range of uncertainties entailed by renewables and highly critical infrastructure failures due to potential cyber-attacks. In this chapter, we look at some of the state-of-the-art technologies to monitor events in...

book chapter 2019

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Economic outage scheduling of transmission line for long-term horizon under demand and wind scenarios

Kulkarni, Raunak R. (author), Khuntia, S.R. (author), Joseph, A. (author), Rueda, José L. (author), Palensky, P. (author)

A substantial increase in renewable energy in-feed to the primary grid as well as demand growth poses a challenge for transmission system operators (TSOs) to perform maintenance activities while addressing security of supply. A computationally efficient outage scheduling algorithm which is customizable in terms of area and time selection is...

conference paper 2018

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Predictive Mitigation of Short Term Voltage Instability Using a Faster Than Real-Time Digital Replica

Joseph, A. (author), Cvetkovic, M. (author), Palensky, P. (author)

Predictive mitigation of undesired events has long been seen as a supportive complement to corrective mitigation that could relax the stringent requirements on the corrective<br/>actions and increase reliability of the overall system. This article describes one such predictive measure, i.e. the use of faster than real-time simulation in...

conference paper 2018

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Prediction of Short-Term Voltage Instability Using a Digital Faster Than Real-Time Replica

Joseph, A. (author), Cvetkovic, M. (author), Palensky, P. (author)

Predictive analysis of post fault system dynamic behavior can be a vital resource for better control and reliability improvement of the overall system. This article presents methods for predictive analysis of Fault Induced Dynamic Voltage Recovery (FIDVR) event using a faster than real-time digital replica of a power system. The methods proposed...

conference paper 2018

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Entrepreneurial intention and the heart of entrepreneurship among technical students

Scholten, V.E. (author), Joseph, B.R. (author), Prickaerts, P (author)

journal article 2012