There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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Modern Applied Statistics With S

On robust estimation of the location parameter

The PASCAL Visual Object Classes Challenge

Matrix Differential Calculus with Applications in Statistics and Econometrics

Understanding how appliances in a house consume power is important when making intelligent and informed decisions about conserving energy. Appliances can turn ON and OFF either by the actions of occupants or by automatic sensing and actuation (e.g., thermostat). It is also difficult to understand how much a load consumes at any given operational state. Occupants could buy sensors that would help, but this comes at a high financial cost. Power utility companies around the world are now replacing old electro-mechanical meters with digital meters (smart meters) that have enhanced communication capabilities. These smart meters are essentially free sensors that offer an opportunity to use computation to infer what loads are running and how much each load is consuming (i.e., load disaggregation). We present a new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference. Our sparse Viterbi algorithm can efficiently compute sparse matrices with a large number of super-states. Additionally, our disaggregator can run in real-time on an inexpensive embedded processor using low sampling rates.

Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring

In region-of-interest (ROI)-based video coding, ROI parts of the frame are encoded with higher quality than non-ROI parts. At low bit rates, such encoding may produce attention-grabbing coding artifacts, which may draw viewer's attention away from ROI, thereby degrading visual quality. In this paper, we present a saliency-aware video compression method for ROI-based video coding. The proposed method aims at reducing salient coding artifacts in non-ROI parts of the frame in order to keep user's attention on ROI. Further, the method allows saliency to increase in high quality parts of the frame, and allows saliency to reduce in non-ROI parts. Experimental results indicate that the proposed method is able to improve visual quality of encoded video relative to conventional rate distortion optimized video coding, as well as two state-of-the art perceptual video coding methods.

Saliency-Aware Video Compression

A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.

AMPds: A public dataset for load disaggregation and eco-feedback research

With the cost of consuming resources increasing (both economically and ecologically), homeowners need to find ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the first dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms.

/pdf/electricity-water-and-natural-gas-consumption-of-a-35tn7xagls.pdf

Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014

Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud. This paradigm, termed collaborative intelligence, involves communicating feature data between the mobile and the cloud. The efficiency of such approach can be further improved by lossy compression of feature data, which has not been examined to date. In this work we focus on collaborative object detection and study the impact of both near-lossless and lossy compression of feature data on its accuracy. We also propose a strategy for improving the accuracy under lossy feature compression. Experiments indicate that using this strategy, the communication overhead can be reduced by up to 70% without sacrificing accuracy.

Ivan V. Bajic

Papers

Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring

Saliency-Aware Video Compression

AMPds: A public dataset for load disaggregation and eco-feedback research

Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014

Deep Feature Compression for Collaborative Object Detection