Chun Tung Chou

Ear-phone: an end-to-end participatory urban noise mapping system

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.

Subspace algorithms for the identification of multivarible dynamic errors-in-variables models

Abstract We consider the problem of identifying multivariable finite dimensional linear time-invariant systems from noisy input/output measurements. Apart from the fact that both the measured input and output are corrupted by additive white noise, the output may also be contaminated by a term which is caused by a white input process noise; furthermore, all these noise processes are allowed to be correlated with each other. We shall develop a solution to this problem in the framework of subspace identification and we shall show that our algorithms give consistent estimates when the system is operating in open- or closed-loop. Two realistic simulation studies are presented to demonstrate the practical applicability of the proposed algorithms.

The design and evaluation of a hybrid sensor network for Cane-Toad monitoring

This paper investigates a wireless, acoustic sensor network application --- monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the Cane Toad (see Fig. 1). This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage.We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as Fast Fourier Transforms (FFTs) and machine learning.The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate.We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

Design and evaluation of a hybrid sensor network for cane toad monitoring

This paper investigates a wireless, acoustic sensor network application-monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate. We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

Linear and non-linear system identification using separable least squares

We demonstrate how the separable least-squares technique of Golub and Pereyra can be exploited in the identification of both linear and non-linear systems based on the prediction error formulation. The model classes to be considered here are the output error model and innovations model in the linear case and the Wiener system in the non-linear case. This technique together with a suitable choice of parametrisation allow us to solve, in the linear case, the associated optimisation problem using only np parameters instead of the usual n(m + p) + mp parameters when canonical forms are used, where n, m and p denote respectively the number of states, inputs and outputs, We also prove under certain assumptions that the separable optimisation method is numerically better conditioned than its non-separable counterpart. Successful operations of these identification algorithms are demonstrated by applying them to two sets of industrial data: an industrial dryer in the linear case and a high-purity distillation column in the non-linear case.

Low-Latency Broadcast in Multirate Wireless Mesh Networks

In a multirate wireless network, a node can dynamically adjust its link transmission rate by switching between different modulation schemes. In the current IEEE802.11a/b/g standards, this rate adjustment is defined for unicast traffic only. In this paper, we consider a wireless mesh network (WMN), where a node can dynamically adjust its link-layer multicast rates to its neighbors, and address the problem of realizing low-latency network-wide broadcast in such a mesh. We first show that the multirate broadcast problem is significantly different from the single-rate case. We will then present an algorithm for achieving low-latency broadcast in a multirate mesh which exploits both the wireless multicast advantage and the multirate nature of the network. Simulations based on current IEEE802.11 parameters show that multirate multicast can reduce broadcast latency by 3-5 times compared with using the lowest rate alone. In addition, we show the significance of the product of transmission rate and transmission coverage area in designing multirate WMNs for broadcast

Energy efficient information collection in wireless sensor networks using adaptive compressive sensing

We consider the problem of using Wireless Sensor Networks (WSNs) to measure the temporal-spatial field of some scalar physical quantities. Our goal is to obtain a sufficiently accurate approximation of the temporal-spatial field with as little energy as possible. We propose an adaptive algorithm, based on the recently developed theory of adaptive compressive sensing, to collect information from WSNs in an energy efficient manner. The key idea of the algorithm is to perform “projections” iteratively to maximise the amount of information gain per energy expenditure. We prove that this maximisation problem is NP-hard and propose a number of heuristics to solve this problem. We evaluate the performance of our proposed algorithms using data from both simulation and an outdoor WSN testbed. The results show that our proposed algorithms are able to give a more accurate approximation of the temporal-spatial field for a given energy expenditure.

Wiener model identification and predictive control for dual composition control of a distillation column

The benefits of using the Wiener model based identification and control methodology presented in this paper, compared to linear techniques, are demonstrated for dual composition control of a moderate–high purity distillation column simulation model. An identification experiment design is presented which enables one to identify both the low and high gain directions of the distillation column, properties which are important for control and hard to identify in a conventional identification experiment setup as is demonstrated in the paper. Data from the proposed experiment design is used for indirect closed-loop identification of both a linear and a Wiener model, which shows the ability of the Wiener model to approximate the nonlinearity of the distillation column much closer than the linear model can. The identified Wiener model is used in a MPC algorithm in which the nonlinearity of the Wiener model is transformed into a polytopic description. In this way a convex optimisation problem is retained while the effect of the nonlinearity on the input–output behaviour of the plant is still taken into account. The performance of the proposed Wiener MPC is compared with linear MPC based on the identified linear models, and with a Wiener MPC in which the nonlinearity of the Wiener model is removed from the control problem via an inversion, a popular way to handle Wiener models in a MPC framework. The simulations demonstrate that the proposed Wiener MPC outperforms the other MPC algorithms.

Automatic Collection of Fuel Prices from a Network of Mobile Cameras

It is an undeniable fact that people want information. Unfortunately, even in todays highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.

Identification of continuous-time MIMO state space models from sampled data, in the presence of process and measurement noise

This paper demonstrates the use of a continuous-time subspace model identification method, in the identification of MIMO state-space models. The measured input and output signals are assumed to be measured at regularly spaced sampling instances. The presence of both measurement and process noise is considered. The proposed method gives a biased estimate of the system matrices, but we shall show how to minimise this bias by a proper choice of the lag used for the instruments. Finally, the applicability of the method is demonstrated in the identification of aircraft dynamics.