Greg Pottie

Entropy-based sensor selection heuristic for target localization

We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing models of a set of candidate sensors for selection, the heuristic selects an informative sensor such that the fusion of the selected sensor observation with the prior target location distribution would yield on average the greatest or nearly the greatest reduction in the entropy of the target location distribution. The heuristic greedily selects one sensor in each step without retrieving any actual sensor observations. The heuristic is also computationally much simpler than the mutual-information-based approaches. The effectiveness of the heuristic is evaluated using localization simulations in which Gaussian sensing models are assumed for simplicity. The heuristic is more effective when the optimal candidate sensor is more informative.

Cochannel interference suppression through time/space diversity

Wireless systems are subject to a time-varying and unknown a priori combination of cochannel interference, fading, and Gaussian noise. It is well known that multiple antennas can provide diversity in space that allows system tradeoffs between interference suppression and mitigation of fading. This paper describes how to achieve these same tradeoffs through diversity in time provided by channel coding. The mathematical description of time diversity is identical to that of space diversity, and what emerges is a unified framework for signal processing. Decoding algorithms are provided for repetition codes, rate 1/n convolutional codes, first-order Reed-Muller codes, and a new class of linear combination codes that provide cochannel interference suppression. In all cases it is possible to trade performance for complexity by choosing between joint estimation and a novel low-complexity linear canceler structure that treats interference as noise. This means that a single code can be used in a variety of system environments just by changing the processing in the receiver.

Feature selection based on mutual information for human activity recognition

In this work, we consider a classification problem of 14 physical activities using a body sensor network (BSN) consisting of 14 tri-axial accelerometers. We use a tree-based classifier, and develop a feature selection algorithm based on mutual information to find the relevant features at every internal node of the tree. We evaluate our algorithm on 31 features per accelerometer (total of 434), and we present the results on 8 subjects with a 96% average accuracy.

Goodput and Delay in Networks with Controlled Mobility

The use of mobility has been shown to be beneficial in wireless ad hoc and sensor networks, for improving communication performance and other functionality. This paper discusses the communication throughput, goodput and delay considerations when a set of mobile nodes is used as relays to transfer data among multiple static nodes. While previous work has considered randomly mobile nodes, we consider controlled mobile agents. Randomly mobile agents are not available in many network scenarios, such as embedded sensor network deployments, and the use of controllably mobile agents has been considered for such networks. We derive results for the worst case delay, throughput and goodput with controllably mobile relays. Our analysis indicates that this scenario differs fundamentally from the random mobility case. This scenario could, however, be used in defense applications for better communications yield. Further, our results are guaranteed to be achieved in a particular topology, as opposed to previous results which are probabilistic for a particular deployment. We also discuss practical algorithms that can be used to control the routes of mobile agents.

Lossy source coding of multiple Gaussian sources: m-helper problem

We consider the network information theoretic problem of finding the rate distortion bound when multiple correlated Gaussian sources are present. One of these is the source of interest but some side information from other sources is also transmitted to help reduce the distortion in the reproduction of the first source. The other sources are treated as helpers and are also coded. Special cases of this problem have been solved before, such as when the reproduction is lossless, when the sources are conditionally independent given one of them, or when the number of helpers is limited to one. We consider a generalized version and show that the previously derived expressions fall out as special cases of our bound. Our results can be directly utilized by designers to choose not only how many of the available sources should actually be communicated but also which sources have the highest potential to reduce the distortion.

Poster abstract: entropy-based sensor selection for localization

We propose a novel entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing characteristics of a set of additional sensors, we would like to select an optimal additional sensor such that fusion of its measurements with existing information would yield the greatest entropy reduction of the target location distribution. The heuristic can select a sub-optimal additional sensor without retrieving the measurements of candidate sensors. The heuristic is computationally much simpler than the mutual information based sensor selection approaches for localization and tracking [1, 2]. Just as those existing approaches do, the heuristic greedily selects one sensor in each step.

Detecting stumbles with a single accelerometer

Falls are a common problem in the elderly population, and their prediction has been a major interest for the medical field. The relationship between stumbles and falls has not been very well understood yet. A critical requirement in advancing the study of this relationship is the realization of a realistic and effective stumble detection system. In this paper, we present a system for the detection of stumbles during walking. Our system consists of a single low cost triaxial accelerometer that may be worn by patients and is convenient for a wide range of subjects. We formulate the problem as an anomaly detection and we validate our system with a large data set collected from 9 subjects. The data set contains a total of 100 stumbles and 45 minutes of walking. We compare 7 different placements for the accelerometer, and show that our system achieves a 99% detection rate, with a 0.2% false alarm rate using an accelerometer worn on the chest.

Estimation of accelerometer orientation for activity recognition

Tri-axial accelerometers have been widely used for human activity recognition and classification. A main challenge in accelerometer-based activity recognition is the system dependence on the orientation of the accelerometer. This paper presents an approach for overcoming this challenge by calibrating the accelerometer orientation using pre-defined activities alongside automated correction algorithms. This method includes manipulation of data via rotation matrices estimated from the pre-defined activities. The system is subsequently tested with real data where sensors were placed in the wrong orientation. A control set of correctly oriented sensors were also placed for validation purposes. We show that our approach improves the accuracy from 38% to 92% for the wrongly oriented sensors, when the control sensors achieve 95%. A GUI was also created in order to make the tool easily available to other researchers.

Channel coding for cochannel interference suppression in wireless communication systems

Cochannel interference is a major impairment in cellular systems. In practice the performance of time and frequency division multiple access (TDMA and FDMA) systems is limited by a few dominant cochannel interferers. Conventional channel coding techniques treat this interference as noise to mitigate its effect. It is shown that cochannel interference can be totally suppressed by using a adaptive nonlinear decoder. We illustrate our ideas using codes in a real (complex) field as well as codes in a finite field. The decoding technique can be combined with antenna diversity to offer increased interference suppression capability.

A robust variable-rate speech coder

The goal of this study is to develop a robust and high-quality speech coder for wireless communication. The proposed coder is a perceptually-based variable-rate subband coder. The perceptual metric ensures that encoding is optimized to the human listener and is based on calculating the signal-to-mask ratio in short-time frames of the input signal. An adaptive bit allocation scheme is employed and the subband energies are then quantized using a Max-Lloyd quantizer. The coder is fully scalable-increasing the bit rates, improves the quality of encoded speech. Subjective listening tests, using quiet and noisy input signals, indicate that the proposed coder produces high-quality speech when operating at 12 kbps or higher. In error-free conditions, our coder has comparable performance to that of QCELP or GSM coders. For speech in background noise, however, our coder, at 12 kbps, outperforms QCELP significantly, and for music, it outperforms both QCELP and GSM.