6,026 research outputs found
Energy Disaggregation via Adaptive Filtering
The energy disaggregation problem is recovering device level power
consumption signals from the aggregate power consumption signal for a building.
We show in this paper how the disaggregation problem can be reformulated as an
adaptive filtering problem. This gives both a novel disaggregation algorithm
and a better theoretical understanding for disaggregation. In particular, we
show how the disaggregation problem can be solved online using a filter bank
and discuss its optimality.Comment: Submitted to 51st Annual Allerton Conference on Communication,
Control, and Computin
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
Integrating Systems and Economic Models for Security Investments in the Presence of Dynamic Stochastic Shocks
Organizations deploy a number of security measures with differing intensities to protect their company’s information assets. These assets are found in various location within a company, with differing levels of security applied to them. Such measures protect the different aspects of the organization’s information systems, which are typically separated into three different attributes; confidentiality, integrity, and availability. We start by defining a system in terms of its locations, resources and processes to use as an underlying framework for our security model. We then systematically define the time evolution of all the three attributes when subjected to shocks aiming at degrading the system’s capacity. We shock each of the attributes of the system and trace the adjustment of the attributes and policy responses; we undertake this exercise for different types of organizations: a military weapons system operator, a financial firm or bank, a retail organization, and a medical research organization, producing their impulse-response functions to quantify their responses and speed of adjustment. This economic model is validated through various means, including Monte Carlo simulations. We find that organizations, although they react in similar ways to shocks to their attributes over time, and are able quickly to get back to their pre-shock states over time, differ in the intensity of their policy responses which differ depending upon the character of the organization
Differentially private MIMO filtering for event streams
Rigorous privacy-preserving mechanisms that can process and analyze dynamic data streams in real-time are required to encourage a wider adoption of many large-scale monitoring and control systems recording the detailed activities of their users, such as intelligent transportation systems, smart grids or smart buildings. Motivated by scenarios where signals originate from many sensors capturing privacy-sensitive events about individuals and several statistics of interest need to be continuously published in real-time, we consider the problem of designing multi-input multi-output (MIMO) systems processing event streams while providing certain differential privacy guarantees on the input signals. We show how to construct and optimize MIMO extensions of the zero-forcing mechanism, which we previously proposed for single-input single-output systems. Some of these extensions can take a statistical model of the input signals into account. We illustrate our privacy-preserving filter design methodology in two examples: privately monitoring and forecasting occupancy in a building equipped with multiple motion detection sensors; and analyzing the activity of a Markov chain model of a simple shared processing server
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