Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

https://www.mdpi.com/1424-8220/12/12/16838/pdf

Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

https://escholarship.org/content/qt8755h7rn/qt8755h7rn.pdf?t=p0jvvs

Occupant behavior modeling for building performance simulation: Current state and future challenges:

The use of the Hilbert transform in ECG signal analysis

An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network

A robust new algorithm for QRS defection using the properties of the Hilbert transform is proposed. The method allows R waves to be differentiated from large, peaked T and P waves with a high degree of accuracy and minimizes the problems associated with baseline drift, motion artifacts and muscular noise. The performance of the algorithm was tested using the records of the MIT-BIH Arrhythmia Database. Beat by beat comparison was performed according to the recommendation of the American National Standard for ambulatory ECG analyzers (ANSI/AAMI EC38-1998). A QRS detection rate of 99.64%, a sensitivity of 99.81% and a positive prediction of 99.83% was achieved against the MIT-BIH Arrhythmia database. The noise tolerance of the new proposed QRS detector was also tested using standard records from the MIT-BIH Noise Stress Test Database. The sensitivity of the detector remains about 94% even for signal-to-noise ratios (SNR) as low as 6 dB.

A new QRS detection algorithm based on the Hilbert transform

Monitoring electricity consumption in the home is an important way to help reduce energy usage and Non-Intrusive Load Monitoring (NILM) techniques are a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. In this paper, we discuss event detection algorithms used in the NILM literature and propose new metrics for evaluating them. In particular, we introduce metrics that incorporate information contained in the power signal instead of strict detection rates. We show that this information is important for NILM applications with the goal of improving appliance energy disaggregation. Our work was carried out on a publicly-available week-long dataset of real residential power usage.

Event detection for Non Intrusive load monitoring

Contemporary office buildings commonly experience changes in occupancy patterns and needs due to changes in business practice and personal churns. Hence, it is important to understand and accurately capture the information of such trends for applications in building design and subsequent building operations. Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security, and occupancy profiles are widely used in building simulations. However, the ability to discern the actual number of people in a space is often beyond the scope of current sensing techniques. This paper presents a study to develop algorithms for occupancy number detection based on the analysis of environmental data captured from existing sensors and ambient sensing networks. Both wireless and wired sensor networks are deployed in the Robert L. Preger Intelligent Workplace (IW) at Carnegie Mellon University, comprising six different types of sensors. An average of 80% accuracy on the occupancy number detection was achieved by Hidden Markov Models during testing periods. The findings also offer encouraging possibilities for incorporating the algorithms into building management systems for optimizing energy use while maintaining occupant comfort.

Diego S. Benitez

Papers

The use of the Hilbert transform in ECG signal analysis

An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network

A new QRS detection algorithm based on the Hilbert transform

Event detection for Non Intrusive load monitoring

Occupancy detection through an extensive environmental sensor network in an open-plan office building