Energy-efficient detection system in time-varying signal and noise power

Le, Long N.; Jun, David; Jones, Douglas L.

doi:10.1109/icassp.2013.6638154

Cited by 9 publications

(12 citation statements)

References 12 publications

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“…4 Profiled on the Android prototype. 5 Profiled in MatLab on the PC. The off-mode/idle energy costs (per audio frame) on the client are given as follows.…”

Section: B Software Componentsmentioning

confidence: 99%

Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems

Jones

2017

IEEE Trans. Circuits Syst. I

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Abstract-Energy-efficiency is highly desirable for sensing systems in the Internet of Things (IoT). A common approach to achieve low-power systems is duty-cycling, where components in a system are turned off periodically to meet an energy budget. However, this work shows that such an approach is not necessarily optimal in energy-efficiency, and proposes guidedprocessing as a fundamentally better alternative. The proposed approach offers 1) explicit modeling of performance uncertainties in system internals, 2) a realistic resource consumption model, and 3) a key insight into the superiority of guided-processing over duty-cycling. Generalization from the cascade structure to the more general graph-based one is also presented. Once applied to optimize a large-scale audio sensing system with a practical detection application, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7× and 4× reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the dutycycling approach.

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“…4 Profiled on the Android prototype. 5 Profiled in MatLab on the PC. The off-mode/idle energy costs (per audio frame) on the client are given as follows.…”

Section: B Software Componentsmentioning

confidence: 99%

Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems

Jones

2017

IEEE Trans. Circuits Syst. I

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“…Since the state variables are integers, this recursion is numerically stable for moderately large values of m0 and m1. The final element of the recursion is R0,0, which is the cost at the beginning of the test (i.e., m0 = m1 = 0) when the optimal decision and stopping rules, given by (17) and (19), respectively, are used and λ and µ are given, i.e., L(λ, µ) = R0,0(λ, µ). Once we are able to evaluate L(λ, µ), the Lagrange multiplies can be determined by solving (13) for λ and µ.…”

Section: Joint Detection and Estimationmentioning

confidence: 99%

“…For example, in speech processing, it was shown in [17], [18] that the performance of voice detection systems can be drastically improved by jointly estimating the noise power and a priori SNR. In [19], it was shown that a scheduling scheme performed detection in an energy-efficient manner by jointly estimating the SNR. However, [20] reported that the techniques developed in some of the papers mentioned above were not readily applicable to the problem of joint detection and signal and noise power estimation.…”

Section: Introductionmentioning

confidence: 99%

“…For a Bayesian formulation, it was shown [20, Sec. III] that knowledge of the priors Pr(H0) and Pr(H1) or of the distribution of the unknown parameters was not amenable for problems addressed in [17] - [19]. Instead, an optimal solution for Gaussian observation models was presented using conjugate priors on the signal and noise powers [20,Sec.…”

Section: Introductionmentioning

confidence: 99%

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Sequential joint signal detection and signal-to-noise ratio estimation

Fauß

Nagananda

Zoubir

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance. This optimization problem is reduced to a more tractable formulation by transforming the observed signal and noise sequences to a single sequence of Bernoulli random variables; joint detection and estimation is then performed on the Bernoulli sequence. This transformation renders the problem easily solvable, and results in a computationally simpler sufficient statistic compared to the one based on the (untransformed) observation sequences. Experimental results demonstrate the advantages of the proposed method, making it feasible for applications having strict constraints on data storage and computation.

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“…Like many system design problems, resource-efficiency is an important objective in the design of multi-task detection systems [3]. A well-known resource-efficient, modular design is the cascade structure, which consists of a collection of detection modules in tandem.…”

Section: Introductionmentioning

confidence: 99%

Feature-Sharing in Cascade Detection Systems With Multiple Applications

Le¹,

Jones²

2017

IEEE J. Sel. Top. Signal Process.

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Abstract-Traditional distributed detection systems are often designed for a single target application. However, with the emergence of the Internet of Things (IoT) paradigm, next-generation systems are expected to be a shared infrastructure for multiple applications. To this end, we propose a modular, cascade design for resource-efficient, multi-task detection systems. Two (classes of) applications are considered in the system, a primary and a secondary one. The primary application has universal features that can be shared with other applications, to reduce the overall feature extraction cost, while the secondary application does not. In this setting, the two applications can collaborate via feature sharing. We provide a method to optimize the operation of the multi-application cascade system based on an accurate resource consumption model. In addition, the inherent uncertainties in feature models are articulated and taken into account. For evaluation, the twin-comparison argument is invoked, and it is shown that, with the optimal feature sharing strategy, a system can achieve 9× resource saving and 1.43× improvement in detection performance.

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Energy-efficient detection system in time-varying signal and noise power

Cited by 9 publications

References 12 publications

Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems

Guided-Processing Outperforms Duty-Cycling for Energy-Efficient Systems

Sequential joint signal detection and signal-to-noise ratio estimation

Feature-Sharing in Cascade Detection Systems With Multiple Applications

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