6736227
VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network
We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-based vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the “bumpiness” of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is fully functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.
6053956
A low-power correlation-derivative CMOS VLSI circuit for bearing estimation
We present a CMOS integrated circuit (IC) for bearing estimation in the low-audio range that performs a correlation derivative approach in a 0.35-/spl mu/m technology. The IC calculates the bearing angle of a sound source with a mean variance of one degree in a 360/spl deg/ range using four microphones: one pair is used to produce the indication and the other to define the quadrant. An adaptive algorithm decides which pair to use depending on the direction of the incoming signal, in such a way to obtain the best estimate. The IC contains two blocks with 104 stages each. Every stage has a delay unit, a block to reduce the clock speed, and a 10-bit UP/DN counter. The IC measures 2 mm by 2.4 mm, and dissipates 600 /spl mu/W at 3.3 V and 200 kHz. It is purely digital and uses a one-bit quantization of the input signals.
2599649
A wake-up detector for an acoustic surveillance sensor network: algorithm and VLSI implementation
We describe a low-power VLSI wake-up detector for use in an acoustic surveillance sensor network. The detection criterion is based on the degree of low-frequency periodicity in the acoustic signal. To this end, we have developed a periodicity estimation algorithm that maps particularly well to a low-power VLSI implementation. The time-domain algorithm is based on the "bumpiness" of the autocorrelation of one-bit version of the signal. We discuss the relationship of this algorithm to the maximum-likelihood estimator for periodicity. We then describe a full-custom CMOS ASIC that implements this algorithm. This ASIC is fully functional and its core consumes 835 nanoWatts. The ASIC was integrated into an acoustic enclosure and tested outdoors on synthesized sounds. This unit was also deployed in a three-node sensor network and tested on ground-based vehicles.
10633458
Field test results for low power bearing estimator sensor nodes
The paper describes experimental results of low power sensor nodes designed to perform bearing estimation. The nodes are intended to form a wireless sensor network able to locate an audio source. Two different nodes are tested: one is based on a cross-correlation derivative integrated circuit (IC), and the other on a gradient flow IC. Implementation details and experimental results of both systems working in a natural environment are presented.
8543826
Analog VLSI spiking neural network with address domain probabilistic synapses
We present an analog VLSI address-event transceiver containing an array of integrate-and-fire neurons and a scheme for implementing a reconfigurable neural network with probabilistic synapses. Neural "spikes" are transmitted through address-event representation-the address of the sending neuron is communicated through an asynchronous request and acknowledgment cycle. Continuous-valued synaptic weights are implemented by probabilistically routing address events. Results from a prototype system with 1024 analog VLSI integrate-and-fire neurons, each with up to 128 probabilistic synapses, demonstrate these concepts in an image processing application.
16600244
Heterogeneous integration of biomimetic acoustic microsystems
We discuss biologically inspired devices and architectures for acoustic processing microsystems. We exploit state of the art integrated microsystems technologies to heterogeneously integrate electronics and micromechanical structures for acoustic sensing and signal processing.
195349341
A global approach to automatic solution of jigsaw puzzles
2216158
Automatic Quality Assessment of Peptide Tandem Mass Spectra
MOTIVATION
A powerful proteomics methodology couples high-performance liquid chromatography (HPLC) with tandem mass spectrometry and database-search software, such as SEQUEST. Such a set-up, however, produces a large number of spectra, many of which are of too poor quality to be useful. Hence a filter that eliminates poor spectra before the database search can significantly improve throughput and robustness. Moreover, spectra judged to be of high quality, but that cannot be identified by database search, are prime candidates for still more computationally intensive methods, such as de novo sequencing or wider database searches including post-translational modifications.


RESULTS
We report on two different approaches to assessing spectral quality prior to identification: binary classification, which predicts whether or not SEQUEST will be able to make an identification, and statistical regression, which predicts a more universal quality metric involving the number of b- and y-ion peaks. The best of our binary classifiers can eliminate over 75% of the unidentifiable spectra while losing only 10% of the identifiable spectra. Statistical regression can pick out spectra of modified peptides that can be identified by a de novo program but not by SEQUEST. In a section of independent interest, we discuss intensity normalization of mass spectra.
46268673
EigenMS: De Novo Analysis of Peptide Tandem Mass Spectra by Spectral Graph Partitioning
We report on a new de novo peptide sequencing algorithm that uses spectral graph partitioning. In this approach, relationships between m/z peaks are represented by attractive and repulsive springs, and the vibrational modes of the spring system are used to infer information about the peaks (such as “likely b-ion” or “likely y-ion”). We demonstrate the effectiveness of this approach by comparison with other de novo sequencers on test sets of ion-trap and QTOF spectra, including spectra of mixtures of peptides. On all data sets we outperform the other sequencers. Along with spectral graph theory techniques, EigenMS incorporates another improvement of independent interest: robust statistical methods for recalibration of time-of-flight mass measurements. Robust recalibration greatly outperforms simple least-squares recalibration, achieving about three times the accuracy for one QTOF data set.
654791
An image signature for any kind of image
We describe an algorithm for computing an image signature, suitable for first-stage screening for duplicate images. Our signature relies on the relative brightness of image regions, and is generally applicable to photographs, text documents, and line art. We give experimental results on the sensitivity and robustness of signatures for actual image collections, and also results on the robustness of signatures under transformations such as resizing, rescanning, and compression.