Magnetic resonance imaging (MRI) detection of the murine brain response to light: temporal differentiation and negative functional MRI changes.

Using a 9.4 T MRI instrument, we have obtained images of the mouse brain response to photic stimulation during a period between deep anesthesia and the early stages of arousal. The large image enhancements we observe (often >30%) are consistent with literature results extrapolated to 9.4 T. However, there are also two unusual aspects to our findings. (i) The visual area of the brain responds only to changes in stimulus intensity, suggesting that we directly detect operations of the M visual system pathway. Such a channel has been observed in mice by invasive electrophysiology, and described in detail for primates. (ii) Along with the typical positive response in the area of the occipital portion of the brain containing the visual cortex, another area displays decreased signal intensity upon stimulation.

Real-time detection of DNA hybridization and melting on oligonucleotide arrays by using optical wave guides.

The challenge of the Human Genome Project is to increase the rate of DNA sequence acquisition by two orders of magnitude to complete sequencing of the human genome by the year 2000. The present work describes a rapid detection method using a two-dimensional optical wave guide that allows measurement of real-time binding or melting of a light-scattering label on a DNA array. A particulate label on the target DNA acts as a light-scattering source when illuminated by the evanescent wave of the wave guide and only the label bound to the surface generates a signal. Imaging/visual examination of the scattered light permits interrogation of the entire array simultaneously. Hybridization specificity is equivalent to that obtained with a conventional system using autoradiography. Wave guide melting curves are consistent with those obtained in the liquid phase and single-base discrimination is facile. Dilution experiments showed an apparent lower limit of detection at 0.4 nM oligonucleotide. This performance is comparable to the best currently known fluorescence-based systems. In addition, wave guide detection allows manipulation of hybridization stringency during detection and thereby reduces DNA chip complexity. It is anticipated that this methodology will provide a powerful tool for diagnostic applications that require rapid cost-effective detection of variations from known sequences.

Re-Examining the Stop-Signal Task to Test Competing Theories of AD/HD

The current study tested two competing models of Attention-Deficit/Hyperactivity Disorder (AD/HD), the inhibition and state regulation theories, by conducting fine-grained analyses of the Stop-Signal Task and another putative measure of behavioral inhibition, the Gordon Continuous Performance Test (G-CPT), in a large sample of children and adolescents. The inhibition theory posits that performance on these tasks reflects increased difficulties for AD/HD participants to inhibit prepotent responses. The model predicts that putative stop-signal reaction time (SSRT) group differences on the Stop-Signal Task will be primarily related to AD/HD participants requiring more warning than control participants to inhibit to the stop-signal and emphasizes the relative importance of commission errors, particularly "impulsive" type commissions, over other error types on the G-CPT. The state regulation theory, on the other hand, proposes response variability due to difficulties maintaining an optimal state of arousal as the primary deficit in AD/HD. This model predicts that SSRT differences will be more attributable to slower and/or more variable reaction time (RT) in the AD/HD group, as opposed to reflecting inhibitory deficits. State regulation assumptions also emphasize the relative importance of omission errors and "slow processing" type commissions over other error types on the G-CPT. Overall, results of Stop-Signal Task analyses were more supportive of state regulation predictions and showed that greater response variability (i.e., SDRT) in the AD/HD group was not reducible to slow mean reaction time (MRT) and that response variability made a larger contribution to increased SSRT in the AD/HD group than inhibitory processes. Examined further, ex-Gaussian analyses of Stop-Signal Task go-trial RT distributions revealed that increased variability in the AD/HD group was not due solely to a few excessively long RTs in the tail of the AD/HD distribution (i.e., tau), but rather indicated the importance of response variability throughout AD/HD group performance on the Stop-Signal Task, as well as the notable sensitivity of ex-Gaussian analyses to variability in data screening procedures. Results of G-CPT analyses indicated some support for the inhibition model, although error type analyses failed to further differentiate the theories. Finally, inclusion of primary variables of interest in exploratory factor analysis with other neurocognitive predictors of AD/HD indicated response variability as a separable construct and further supported its role in Stop-Signal Task performance. Response variability did not, however, make a unique contribution to the prediction of AD/HD symptoms beyond measures of motor processing speed in multiple deficit regression analyses. Results have implications for the interpretation of the processes reflected in widely-used variables in the AD/HD literature, as well as for the theoretical understanding of AD/HD.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Near Real-Time Detection Of Pipe Bursts in Water Distribution Systems

The research work presented in the thesis describes a new methodology for the automated near real-time detection of pipe bursts in Water Distribution Systems (WDSs). The methodology analyses the pressure/flow data gathered by means of SCADA systems in order to extract useful informations that go beyond the simple and usual monitoring type activities and/or regulatory reporting , enabling the water company to proactively manage the WDSs sections. The work has an interdisciplinary nature covering AI techniques and WDSs management processes such as data collection, manipulation and analysis for event detection. Indeed, the methodology makes use of (i) Artificial Neural Network (ANN) for the short-term forecasting of future pressure/flow signal values and (ii) Rule-based Model for bursts detection at sensor and district level. The results of applying the new methodology to a District Metered Area in Emilia- Romagna’s region, Italy have also been reported in the thesis. The results gathered illustrate how the methodology is capable to detect the aforementioned failure events in fast and reliable manner. The methodology guarantees the water companies to save water, energy, money and therefore enhance them to achieve higher levels of operational efficiency, a compliance with the current regulations and, last but not least, an improvement of customer service.

Error detection and correction in binary parallel digital computers /

"Retyped October, 1964"

Non-invasive Detection of Cortical Control Signals for Brain-Computer Interfaces

Thesis (Ph.D.)--University of Washington, 2016-05

Marine mammal species detection and classification

Thesis (Ph.D.)--University of Washington, 2016-04

Optimized Decoding for Auditory Attention Detection

Thesis (Master's)--University of Washington, 2016-06

Ultra-wideband multistatic SAR for the detection and location of landmines

Techniques for improving the signal to clutter ratio of an. ultra-wideband SAR designed to detect small mine-like objects in the surface of the ground were investigated. In particular, images were collected using different bistatic antenna configurations in an attempt to decorrelate the clutter with respect to the targets. The images were converted to a reference depression angle, summed, and then converted to ground coordinates. The resulting target strengths were then compared with the amplitude distribution of the ground clutter to show the improvement obtained. While some improvement was demonstrated, this was for the relatively easy scenario of targets on the surface partially obscured by grass. Detection based on thresholding the raw RF signal (the bipolar response) rather than the envelope (baseband I-2 + Q(2)) was also considered to further enhance target-to-clutter ratios.

Fourier-domain optical coherence tomography: optimization of signal-to-noise ratio in full space

Optical coherence tomography (OCT) is an emerging coherence-domain technique capable of in vivo imaging of sub-surface structures at millimeter-scale depth. Its steady progress over the last decade has been galvanized by a breakthrough detection concept, termed spectral-domain OCT, which has resulted in a dramatic improvement of the OCT signal-to-noise ratio of 150 times demonstrated for weakly scattering objects at video-frame-rates. As we have realized, however, an important OCT sub-system remains sub-optimal: the sample arm traditionally operates serially, i.e. in flying-spot mode. To realize the full-field image acquisition, a Fourier holography system illuminated with a swept-source is employed instead of a Michelson interferometer commonly used in OCT. The proposed technique, termed Fourier-domain OCT, offers a new leap in signal-to-noise ratio improvement, as compared to flying-spot OCT systems, and represents the main thrust of this paper. Fourier-domain OCT is described, and its basic theoretical aspects, including the reconstruction algorithm, are discussed. (C) 2004 Elsevier B.V. All rights reserved.

Reply to "Comment on 'Signal correlation between two normal-mode helical antennas for diversity reception in a multipath environment'"

This comment points out an inaccurate formula relating the signal correlation coefficient to the mutual impedance and corrects it. © 2005 IEEE.

Seizure detection algorithm for neonates based on wave-sequence analysis

Objective: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. Methods: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. Results: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30 s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. Conclusions: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. Significance: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring. Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.

The detection of environmental autoinducing peptide quorum-sensing genes from an uncultured Clostridium sp in landfill leachate reactor biomass

Aims: To elucidate whether a dominant uncultured clostridial (Clostridium thermocellum-like) species in an environmental sample (landfill leachate), possesses an autoinducing peptide (AIP) quorum-sensing (QS) gene, although it may not be functional. Methods and Results: A modified AIP accessory gene regulator (agr)C PCR protocol was performed on extracted DNA from a landfill leachate sample (also characterized by 16S rRNA gene cloning) and the PCR products were cloned, sequenced and phylogenetically analysed. It appeared that two agrC gene phylotypes existed, most closely related to the C. thermocellum agrC gene, differing by only 1 bp. Conclusions: It is possible to specifically identify and characterize the agrC AIP QS gene from uncultured Firmicutes (C. thermocellum-like) bacteria derived from environmental (landfill leachate) sample. Significance and Impact of the Study: This is the first successful attempt at identifying AIP QS genes from a cellulolytic environment (landfill). The agrC gene was identified as being most closely related to the C. thermocellum agrC gene, the same bacterium identified as being dominant, according to 16S rRNA gene cloning and subsequently fluorescence in situ hybridization analyses, in the same biomass.

Computational methods for the detection of facial palsy

We are developing a telemedicine application which offers automated diagnosis of facial (Bell's) palsy through a Web service. We used a test data set of 43 images of facial palsy patients and 44 normal people to develop the automatic recognition algorithm. Three different image pre-processing methods were used. Machine learning techniques (support vector machine, SVM) were used to examine the difference between the two halves of the face. If there was a sufficient difference, then the SVM recognized facial palsy. Otherwise, if the halves were roughly symmetrical, the SVM classified the image as normal. It was found that the facial palsy images had a greater Hamming Distance than the normal images, indicating greater asymmetry. The median distance in the normal group was 331 (interquartile range 277-435) and the median distance in the facial palsy group was 509 (interquartile range 334-703). This difference was significant (P