CD-HOC: Indoor Human Occupancy Counting using Carbon Dioxide Sensor Data

Human occupancy information is crucial for any modern Building Management System (BMS). Implementing pervasive sensing and leveraging Carbon Dioxide data from BMS sensor, we present Carbon Dioxide - Human Occupancy Counter (CD-HOC), a novel way to estimate the number of people within a closed space from a single carbon dioxide sensor. CD-HOC de-noises and pre-processes the carbon dioxide data. We utilise both seasonal-trend decomposition based on Loess and seasonal-trend decomposition with moving average to factorise carbon dioxide data. For each trend, seasonal and irregular component, we model different regression algorithms to predict each respective human occupancy component value. We propose a zero pattern adjustment model to increase the accuracy and finally, we use additive decomposition to reconstruct the prediction value. We run our model in two different locations that have different contexts. The first location is an academic staff room and the second is a cinema theatre. Our results show an average of 4.33% increment in accuracy for the small room with 94.68% indoor human occupancy counting and 8.46% increase for the cinema theatre in comparison to the accuracy of the baseline method, support vector regression.Comment: 24 page

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems

STEAP: simultaneous trajectory estimation and planning

We present a unified probabilistic framework for simultaneous trajectory estimation and planning (STEAP). Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient. The key idea is to compute the full continuous-time trajectory from start to goal at each time-step. While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem. Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Our approach can contend with high-degree-of-freedom (DOF) trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time. We evaluate our framework empirically in both simulation and on a mobile manipulator.Comment: Published in Autonomous Robot

Sampling-based Incremental Information Gathering with Applications to Robotic Exploration and Environmental Monitoring

In this article, we propose a sampling-based motion planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly-exploring information gathering algorithms and benefits from advantages of sampling-based optimal motion planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.Comment: Revision submitted to IJRR, 49 page

Gaussian Processes Semantic Map Representation

In this paper, we develop a high-dimensional map building technique that incorporates raw pixelated semantic measurements into the map representation. The proposed technique uses Gaussian Processes (GPs) multi-class classification for map inference and is the natural extension of GP occupancy maps from binary to multi-class form. The technique exploits the continuous property of GPs and, as a result, the map can be inferred with any resolution. In addition, the proposed GP Semantic Map (GPSM) learns the structural and semantic correlation from measurements rather than resorting to assumptions, and can flexibly learn the spatial correlation as well as any additional non-spatial correlation between map points. We extend the OctoMap to Semantic OctoMap representation and compare with the GPSM mapping performance using NYU Depth V2 dataset. Evaluations of the proposed technique on multiple partially labeled RGBD scans and labels from noisy image segmentation show that the GP semantic map can handle sparse measurements, missing labels in the point cloud, as well as noise corrupted labels.Comment: Accepted for RSS 2017 Workshop on Spatial-Semantic Representations in Robotic

Extended Existence Probability Using Digital Maps for Object Verification

A main task for automated vehicles is an accurate and robust environment perception. Especially, an error-free detection and modeling of other traffic participants is of great importance to drive safely in any situation. For this purpose, multi-object tracking algorithms, based on object detections from raw sensor measurements, are commonly used. However, false object hypotheses can occur due to a high density of different traffic participants in complex, arbitrary scenarios. For this reason, the presented approach introduces a probabilistic model to verify the existence of a tracked object. Therefore, an object verification module is introduced, where the influences of multiple digital map elements on a track's existence are evaluated. Finally, a probabilistic model fuses the various influences and estimates an extended existence probability for every track. In addition, a Bayes Net is implemented as directed graphical model to highlight this work's expandability. The presented approach, reduces the number of false positives, while retaining true positives. Real world data is used to evaluate and to highlight the benefits of the presented approach, especially in urban scenarios

Event Detection and Predictive Maintenance using Component Echo State Networks

With a growing number of sensors collecting information about systems in indus- try and infrastructure, one wants to extract useful information from this data. The goal of this project is to investigate the applicability of Echo State Net- work techniques to time-varying classification of multivariate time series from primarily mechanical and electrical systems. Two relevant technical problems are predicting impending failure of systems (predictive maintenance), and clas- sifying a common event related to the system (event detection). In this project, they are formulated as a supervised machine learning problem on a multivariate time series. For this problem, Echo State Networks (ESN) have proven effective. However, applying these algorithms to new data sets involves a lot of guesswork as to how the algorithm should be configured to model the data effectively. In this work, a modification of the Echo State Network (ESN) model is presented, that helps to remove some of this guesswork. The new algorithm uses specifically structured components in order to facilitate the generation of relevant features by the ESN. The algorithm is tested on two easy event detection data sets, and one hard predictive maintenance data set. The results are compared to Support Vector Machine and Multilayer Perceptron classifiers, as well as to a basic ESN, which is also implemented as a reference. The component ESN successfully generates promising features, and outperforms the minimum complexity ESN as well as the standard classifiers

Understanding the Role of Dynamics in Brain Networks: Methods, Theory and Application

The brain is inherently a dynamical system whose networks interact at multiple spatial and temporal scales. Understanding the functional role of these dynamic interactions is a fundamental question in neuroscience. In this research, we approach this question through the development of new methods for characterizing brain dynamics from real data and new theories for linking dynamics to function. We perform our study at two scales: macro (at the level of brain regions) and micro (at the level of individual neurons). In the first part of this dissertation, we develop methods to identify the underlying dynamics at macro-scale that govern brain networks during states of health and disease in humans. First, we establish an optimization framework to actively probe connections in brain networks when the underlying network dynamics are changing over time. Then, we extend this framework to develop a data-driven approach for analyzing neurophysiological recordings without active stimulation, to describe the spatiotemporal structure of neural activity at different timescales. The overall goal is to detect how the dynamics of brain networks may change within and between particular cognitive states. We present the efficacy of this approach in characterizing spatiotemporal motifs of correlated neural activity during the transition from wakefulness to general anesthesia in functional magnetic resonance imaging (fMRI) data. Moreover, we demonstrate how such an approach can be utilized to construct an automatic classifier for detecting different levels of coma in electroencephalogram (EEG) data. In the second part, we study how ongoing function can constraint dynamics at micro-scale in recurrent neural networks, with particular application to sensory systems. Specifically, we develop theoretical conditions in a linear recurrent network in the presence of both disturbance and noise for exact and stable recovery of dynamic sparse stimuli applied to the network. We show how network dynamics can affect the decoding performance in such systems. Moreover, we formulate the problem of efficient encoding of an afferent input and its history in a nonlinear recurrent network. We show that a linear neural network architecture with a thresholding activation function is emergent if we assume that neurons optimize their activity based on a particular cost function. Such an architecture can enable the production of lightweight, history-sensitive encoding schemes

DLOREAN: Dynamic Location-aware Reconstruction of multiway Networks

This paper presents a method for learning time-varying higher-order interactions based on node observations, with application to short-term traffic forecasting based on traffic flow sensor measurements. We incorporate domain knowledge into the design of a new damped periodic kernel which lever- ages traffic flow patterns towards better structure learning. We introduce location-based regularization for learning models with desirable geographical properties (short-range or long-range interactions). We show using experiments on synthetic and real data, that our approach performs better than static methods for reconstruction of multiway interactions, as well as time-varying methods which recover only pair-wise interactions. Further, we show on real traffic data that our model is useful for short-term traffic forecasting, improving over state-of-the-art

Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries

Interactive analytics increasingly involves querying for quantiles over sub-populations of high cardinality datasets. Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles, but summary merge times can be a bottleneck during aggregation. We show how a compact and efficiently mergeable quantile sketch can support aggregation workloads. This data structure, which we refer to as the moments sketch, operates with a small memory footprint (200 bytes) and computationally efficient (50ns) merges by tracking only a set of summary statistics, notably the sample moments. We demonstrate how we can efficiently and practically estimate quantiles using the method of moments and the maximum entropy principle, and show how the use of a cascade further improves query time for threshold predicates. Empirical evaluation on real-world datasets shows that the moments sketch can achieve less than 1 percent error with 15 times less merge overhead than comparable summaries, improving end query time in the MacroBase engine by up to 7 times and the Druid engine by up to 60 times.Comment: Technical Report for paper to be published in VLDB 201