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.
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.
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.
36350590
Spike communication of dynamic stimuli: rate decoding versus temporal decoding
Abstract We compare two methods for extracting information about a dynamic analog stimulus from a spike train: a rate decoder, which is based on the number of spikes in a counting window, and a temporal decoder, which is based on interspike intervals. We derive analytical expressions for the distortion between a stimulus current that drives an integrate-and-fire spike generator and a reconstruction of that current based on the spike train. We consider intrinsic distortion, which arises due to the limitations of spike coding, and extrinsic distortion, which is due to explicit alterations of the spike train. We find that in the absence of extrinsic distortion, the temporal decoder is superior to the rate decoder. In the presence of extrinsic distortion, the temporal decoder is preferred when low latency is required, but the rate decoder is preferred when the latency requirement is relaxed.
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.
14474778
Spike Timing-Dependent Plasticity in the Address Domain
Address-event representation (AER), originally proposed as a means to communicate sparse neural events between neuromorphic chips, has proven efficient in implementing large-scale networks with arbitrary, configurable synaptic connectivity. In this work, we further extend the functionality of AER to implement arbitrary, configurable synaptic plasticity in the address domain. As proof of concept, we implement a biologically inspired form of spike timing-dependent plasticity (STDP) based on relative timing of events in an AER framework. Experimental results from an analog VLSI integrate-and-fire network demonstrate address domain learning in a task that requires neurons to group correlated inputs.
12319077
Paper position sensing
We describe position sensing algorithms used in an airjet paper moving system built at Xerox PARC. The algorithms combine inputs from optical sensors and compute the rectangle best fitting the data. We consider two different types of sensors: fax scan bars and disk-shaped area sensors. Our algorithms proved to be quite effective both in simulation and in actual use.
4796791
A global approach to automatic solution of jigsaw puzzles
We present a new algorithm for automatically solving jigsaw puzzles by shape alone. The algorithm can solve more difficult puzzles than could be solved before, without the use of backtracking or branch-and-bound. The algorithm can handle puzzles in which pieces border more than four neighbors, and puzzles with as many as 200 pieces. Our overall strategy follows that of previous algorithms but applies a number of new ideas, such as robust fiducial points, "highest- confidence-first" search, and frequent global reoptimization of partial solutions.
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.
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.
195349341
A global approach to automatic solution of jigsaw puzzles