David M. Jun

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Low-power, scalable detection systems require aggressive techniques to achieve energy efficiency. Algorithmic methods that can reduce energy consumption by compromising performance are known as being energy-aware. We propose a framework that imposes energy-awareness on cascaded detection algorithms. This is done by setting the detectors' thresholds to make(More)
In many detection applications with battery powered or energy-harvesting sensors, energy constraints preclude the use of the optimal detector all the time. Optimal energy-performance trade-off is therefore needed in such situations. In many applications, the signal and noise power may vary greatly over time, and this can be exploited to constrain energy(More)
Sensing systems with multiple sensors and operating modes warrant active management techniques to balance estimation quality and measurement costs. Existing literature shows that in the joint sensor-scheduling and state-estimation problem for HMMs, estimator optimization can be done independently of the scheduler at each time step. We investigate the(More)
This paper proposes a low-power acoustic sensor built using off-the-shelf components. To reduce energy consumption, the ADC and the microphone's signal-conditioning circuit are replaced by a low-power analog comparator with adjustable thresholds. Although the SNR of the proposed sensor is reduced, we demonstrate how recent advancements in adaptive sensor(More)
Brain-computer interfaces (BCI) utilizing steady-state visually evoked potentials (SSVEP) recorded by electroencephalography (EEG) have exciting potential to enable new systems for disabled individuals and novel controls for robotic and computer systems. To interact with SSVEP-based BCIs, users attend to visual stimuli modulated at predetermined(More)
Current trends and popularity in embedded systems present an opportunity to modernize real-time digital signal processing (DSP) education. In this paper, we describe a hybrid real-time DSP laboratory course that starts with a fixed-point DSP processor to teach sample-by-sample processing, and then switches to the Google Nexus 7 tablet for block-based(More)
This paper presents a new Q-value approximation algorithm for joint sensor scheduling and MAP state estimation in hidden Markov models. The proposed algorithm is motivated by the fact that energy-constrained embedded devices spend a significant amount of time in sleep modes. To develop an adaptive sensing-resource scheduling policy, the proposed base policy(More)
Real-time detection of intermittent events requires continual monitoring and processing of sensor data. A battery-powered device that supports multiple sensing modalities and processing algorithms has the potential to save energy by using expensive sensors and algorithms only when the event of interest is most likely to occur. To develop a policy for(More)
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