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Indirect measurements of physical parameters of interest often require a mathematical model in which these parameters are estimated accordingly to the gathered measurements. Within the Least Squares estimation, the parameters are estimated through a regression problem. The presence of dynamics, multiple sensors and high sampling rates lead to high dimensional regression matrices. This paper deals...
Solutions to real world robotic tasks often require complex behaviors in high dimensional continuous state and action spaces. Reinforcement Learning (RL) is aimed at learning such behaviors but often fails for lack of scalability. To address this issue, Hierarchical RL (HRL) algorithms leverage hierarchical policies to exploit the structure of a task. However, many HRL algorithms rely on task specific...
Global 3D point cloud registration has been solved by finding putative matches between the point clouds for establishing alignment hypotheses. A naive approach would try to perform exhaustive search of triplets with a cubic runtime complexity in the number of data points. Super4PCS reduces this complexity to linear by making use of sets of 4 coplanar points. This paper proposes 2-Point-Normal Sets...
Actor-Critic algorithms have been increasingly researched for tackling challenging reinforcement learning problems. These algorithms are usually composed of two distinct learning processes, namely actor (a.k.a, policy) learning and critic (a.k.a, value function) learning. Actor learning is heavily dependent on critic learning; particularly unreliable critic learning due to its divergence can significantly...
The estimation of the noise power is a core issue in wireless communication systems. In broadcasting, every OFDM frame starts with a preamble symbol, which facilitates the noise power estimation. However, the performance of preamble-based noise estimation schemes worsens in fast-changing environments and cannot efficiently track the noise variation well. In order to track the noise variation symbol...
This paper provides a mathematical method for airspace capacity estimation. It is motivated by the need to assess the impact of unmanned aircraft systems on low altitude airspace operations. We define capacity as a minimum of metric-specific phase transition thresholds. The definition is flexible to accommodate a wide variety of metrics defined for the airspace and hence, can be used to compare different...
Software Estimation is an important part of every Software Engineers’ skill set.At Stevens Institute of Technology, we have taught Estimation as part of our Software Engineering Masters Program since 2001.Over the past few years, we have evolved our teaching style to be more experiential and engaging. This case study describes an evolving software engineering pedagogical method using LEGOs, which...
Fractal features of the electroencephalogram (EEG) signal are useful for characterizing the temporal complexity of non-stationary signals. The present study proposed an event-related complexity analysis (ERC) method for detecting the time-locked changes in the diversity of neural activity. This information is important for investigating higher cognitive functions, such as language. ERC method was...
As compared to the FFT, the recently introduced Sparse Fourier Transform (SFT) achieves substantial reduction in the complexity of detecting frequencies in signals that are sparse in the frequency domain. However, the SFT requires the significant frequencies to be on the grid and the exact sparsity of the signal to be known. In this paper, we propose a framework that overcomes these issues. Our method...
In this paper, we present a technique for reducing the size of the dictionary in sparse signal reconstruction by formulating an initial dictionary containing elements that spans bands of the considered parameter space. We allow for the use of this banded dictionary in a first-stage estimation procedure, in which large parts of the parameter space is discarded for further analysis, thereby reducing...
Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets. Prior to the inference stage, the approach suggests performing dimensionality reduction by first multiplying each data vector by a random Gaussian matrix, and then computing an element-wise sinusoid. Theoretical analysis shows that collecting a sufficient number of such features can be reliably...
The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compressive version of K-means (CKM), that estimates cluster centers from a sketch, i.e. from a drastically compressed representation of the training dataset. We demonstrate empirically that CKM performs similarly to Lloyd-Max,...
This paper provides bounds on the sample complexity of estimating Kronecker-structured dictionaries for Kth-order tensor data. The training samples are generated by linear combinations of these structured dictionary atoms and observed through white Gaussian noise. The lower bound follows from a lower bound on the minimax risk for general coefficient distributions and can be further specialized to...
As part of an ongoing research into extracting mission-critical information from Search and Rescue speech communications, a corpus of unscripted, goal-oriented, two-party spoken conversations has been designed and collected. The Sheffield Search and Rescue (SSAR) corpus comprises about 12 hours of data from 96 conversations by 24 native speakers of British English with a southern accent. Each conversation...
This paper presents an efficient optimization technique for super-resolution two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new formulation of atomic norm minimization (ANM). ANM allows gridless angle estimation for correlated sources even when the number of snapshots is far less than the antenna size, yet it incurs huge computational cost in 2D processing. This paper...
The evaluation dependencies of the high-speed parallel ADC hardware, time, structural and functional complexity on the range of the output signal quantization and binary codes capacity were received. The prospects of mass production and use of this type of ADC in digital equipment and microelectronic technology were shown.
Most algorithms for identifying multiple rumor or infection sources require prior knowledge of the number of sources, or at least an upper bound on the number of sources. In this paper, we consider a deterministic Susceptible-Infected (SI) spreading model in which infection at different source nodes may start at different times. We introduce the concept of a minimal and linear cover of the infection...
In millimeter-wave massive multiple-input multiple-output systems, to decrease the large training overhead of traditional channel estimation techniques, compressive sensing (CS) is advocated for channel estimation by exploiting the channels' sparse nature. However, existing CS-based channel estimation (CSCE) methods have to deal with a large-size reconstruction problem for sparse channel recovery,...
MIMO-OFDM is commonly used for communication system because it provides high data rates and fading effect which is introduced by multipath propagation; the system is robust against it. In MIMO-OFDM, channel estimation is a crucial technique for the estimation of the transmitted signal bits upon the received signal bits. Designing pilot pattern is an important step to realize PSA-OFDM. For channel...
According to a recent report [1], in 2015 alone, the revenue generated from mobile applications is expected to reach $100 billion. This figure indicates that one cannot neglect the potential of mobile application development business. Yet, despite this huge potential there is no calibrated and validated model for estimating the effort required to develop a new mobile application. In this paper, we...
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