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Scene understanding is a crucial requirement for robot navigation. Conditional Random Fields (CRF) are commonly used to solve the scene labelling problem since they represent contextual information efficiently and provide efficient inference methods. However, when a robot navigates through an unknown environment, it is often necessary to adjust the parameters of the CRF online to maintain the same...
In this paper, a model-based event-triggered control law is adopted to reduce communication traffic in networked control systems. In this model-based control system, a nominal model of system plant is used to estimate the system state between two sampling instants. By simultaneously considering bounded system noise and bounded network delay, a mixed event-triggering mechanism is proposed to stabilize...
Both traditional wireless sensor networks and novel crowdsensing techniques generate tremendous real- time data, which provides great opportunities for real- time and long-term analytics. However, how to integrate the heterogeneous data sources and create data analytics toolboxes that can be connected together to solve various problems in urban environment remains open problems. As a PhD student,...
Reinforcement learning comprises an attractive solution to the multi-agent cooperation problem, due to its robustness for learning in unknown and uncertain environments. The objective of this paper is to provide learning capabilities to a group of autonomous agents in order to efficiently perform a cooperative foraging task in a distributed manner. Firstly, the D-DCM-Multi-Q learning method, presented...
In body sensor networks, the need to brace sensing devices firmly to the body raises a fundamental barrier to usability. In this paper, we examine the effects of sensing from devices that do not face this mounting limitation. With sensors integrated into common pieces of clothing, we demonstrate that signals in such free-mode body sensor networks are contaminated heavily with motion artifacts leading...
Flying animals are so well adapted to their complex natural environment, that their aerial locomotive flight mechanisms attract much attention in the context of designing high performance flying robots. Inspired from the sensing and flight control strategies of birds and bats, a bioinspired robust tracking control method for small unmanned air vehicles using in-situ measured airflow information, is...
In this paper we present an integrated approach to control and sensing design. The framework assumes sensor noise as a design variable along with the controller and determines l1 regularized optimal sensing precision that satisfies a given closed loop performance in the presence of model uncertainty. We pursue two approaches here. In the first approach, we represent the uncertainty as polytopic and,...
We study sparse gross error correction for state estimation in a non-linear sensing system. We consider a practical assumption that gross errors are sparse, and their locations tend to be invariant over a few consecutive measurement periods. Under the assumption, a robust state estimation and error correction algorithm using multiple measurement vectors is proposed based on local linear approximation...
Time-frequency (T-F) masking algorithms are focused at separating multiple sound sources from binaural reverberant speech mixtures. The statistical modelling of binaural cues i.e. interaural phase difference (IPD) and interaural level difference (ILD) is a significant aspect of such algorithms. In this paper, a Gaussian-Student's t distribution combined mixture model is exploited for robust binaural...
An effective method is proposed to estimate the desired-signal (S) subspace by the intersection between the signal-plus-interference (SI) subspace and a reference space covering the angular region where the desired signal is located. The estimated S subspace is robust to steering vector mismatch and overestimation of the SI subspace, capable of detecting the relative strength of the desired signal...
Compressed sensing refers to the recovery of high-dimensional but sparse vectors from a small number of measurements. The original and popular approach to compressed sensing is based on li-norm, popularly referred to as the LASSO formulation. A recent paper gives the "best possible" bounds on when the LASSO formulation is able to achieve compressed sensing. Over the years, the traditional...
In this paper, an observer-based approach is proposed to asymptotically identify the velocity and range of feature points on a moving object using a static-moving camera system. Specifically, the system is composed of a static camera and a moving camera, and the approach is divided into two steps. Firstly, utilizing the static camera, a nonlinear observer is designed to identify the up-to-a-scale...
Grid tied inverters and associated control techniques have gained importance in the domain of distributed generation. Different methods of control have been compared in literature depending upon the ability to meet THD limits, damping offered to resonant oscillations and stable operation. Multiple loop methods compared to their single-loop counterparts have proved to be highly effective for meeting...
With the evolution of 5G wireless communication systems, we have witnessed an increase in the demand for wireless broadband applications and services. However, fixed allocation of the frequency spectrum has led to an under-utilization of the spectral resources, making it hard to find unoccupied bands to deploy new services. To address the spectrum scarcity problem, a new and promising technology has...
In this paper, a new active learning scheme is proposed for linear regression problems with the objective of resolving the insufficient training data problem and the unreliable training data labeling problem. A pool-based active regression technique is applied to select the optimal training data to label from the overall data pool. Then, compressive sensing is exploited to remove labeling errors if...
Parking-management systems, including services that recognize vacant stalls, can play a valuable role in reducing traffic and energy waste in large cities. Visual methods for detecting vacant parking spots are cost-effective options since they can take advantage of the cameras already available in many parking lots. However, visual-detection methods can be fragile and not easily generalizable. In...
In this paper, we investigate the problem of robust congestion control in infrastructure-based cognitive radio networks (CRN). We develop an active queue management (AQM) algorithm, termed MAQ, based on multiple model predictive control (MMPC). The goal is to stabilize the TCP queue at the base station (BS) under disturbances from the varying service capacity for secondary users (SU). The proposed...
In the big data era, it's important to identify trustworthy information from an influx of noisy data contributed by unvetted sources from online social media (e.g., Twitter, Instagram, Flickr). This task is referred to as truth discovery which aims at identifying the reliability of the sources and the truthfulness of claims they make without knowing either of them a priori. There are two important...
It is generally well known that the overall performance of the most widely used types of unsupervised change detection methods, based on the luminance pixel-wise difference, is mainly relied on the quality of the so-called difference image and the accuracy of the classification method. In order to address these two issues, this work proposes to first estimate, a new and robust similarity feature map,...
Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour. Pervasive sensors, such as wearable devices, have an increasing market penetration and generate a tremendous amount of data. The myriad of available clinical and consumer-grade wearables generate a continuous time series of a person's daily physical exertion and rest. Applying HAR to the activity time series can...
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