On-line PD detection in power cables using matched filters

On-line measuring of partial discharge (PD) is generally impeded by noise, and in many cases PDs cannot be detected without filtering. Solely the pulse propagation path and the response of the detection circuit determine the shape of a PD pulse; therefore a PD can be regarded as a deterministic signal embedded in additive noise. The optimal filter for this class of signals is the matched filter, which maximises the signal-to-noise ratio at the filter output. Matched filters allow making reliable observations of PD signals embedded in noise and precise estimations of signal parameters, such as arrival time and charge. In order to obtain PD matched filters; knowledge of the PD wave shapes is crucial. A cable propagation model provides such knowledge, and matched filters can be designed specifically for the cable under test. Moreover, by estimating noise statistics the matched filters can be tailored for a practical measuring situation. Experimental results show that partial discharge detection greatly benefits from matched filtering

Statistics of the radiated field of a space-to-earth microwave power transfer system

Statistics such as average power density pattern, variance of the power density pattern and variance of the beam pointing error are related to hardware parameters such as transmitter rms phase error and rms amplitude error. Also a limitation on spectral width of the phase reference for phase control was established. A 1 km diameter transmitter appears feasible provided the total rms insertion phase errors of the phase control modules does not exceed 10 deg, amplitude errors do not exceed 10% rms, and the phase reference spectral width does not exceed approximately 3 kHz. With these conditions the expected radiation pattern is virtually the same as the error free pattern, and the rms beam pointing error would be insignificant (approximately 10 meters)

Developmental changes in sound localization precision under conditions of the precedence effect.

The ability to give perceptual priority to an original sound source and ignore later-arriving echoes of that sound is termed the law of the first wave-front , or the precedence effect (PE). Little attention has been paid to the influence that echoes exert on localization accuracy for the leading sound. The present study investigated localization precision of children and adults in the presence of a simulated echo, using the minimal audible angle (MAA) task, which indicates the smallest change in the location of a sound that can be reliably discriminated. Three age groups were tested: 18-months, 5-years, and adults. Each age group was tested with one single-source (SS) stimulus, and two precedence effect (PE) stimuli: LEAD, in which the original sound shifted from midline and the echo remained at midline, and LAG, where the reverse occurred. Subjects were tested using an adaptive, 2-down/1-up, psychophysical algorithm. For all age groups, MAA thresholds were smallest for SS, larger for LEAD and largest for LAG. For all three stimulus conditions, the 18-month-olds\u27 thresholds were significantly larger than those of either 5-year-olds or adults. Five-year-olds\u27 MAA thresholds for SS sounds were very near to those of adults. However, their thresholds for the PE stimuli were significantly higher than those of adults\u27, and closer to those of 18-month-olds. When a lagging sound is inaudible as a separate auditory event, the auditory system presumably treats the leading and lagging sound as components of the same auditory percept, and uses both signals to compute the position of the sound source. The leading sound, which signals the onset of an auditory event, is assigned perceptual dominance thereby diminishing the nervous system\u27s interaural sensitivity for the later-arriving echo. This and related work has raised important questions concerning the neural mechanisms involved in spatial hearing in adults and children, especially those aspects which involve an active suppression of superfluous signals

Project OASIS: The Design of a Signal Detector for the Search for Extraterrestrial Intelligence

An 8 million channel spectrum analyzer (MCSA) was designed the meet to meet the needs of a SETI program. The MCSA puts out a very large data base at very high rates. The development of a device which follows the MCSA, is presented

Widespread and lateralized social brain activity for processing dynamic facial expressions

Dynamic facial expressions of emotions constitute natural and powerful means of social communication in daily life. A number of previous neuroimaging studies have explored the neural mechanisms underlying the processing of dynamic facial expressions, and indicated the activation of certain social brain regions (e.g., the amygdala) during such tasks. However, the activated brain regions were inconsistent across studies, and their laterality was rarely evaluated. To investigate these issues, we measured brain activity using functional magnetic resonance imaging in a relatively large sample (n = 51) during the observation of dynamic facial expressions of anger and happiness and their corresponding dynamic mosaic images. The observation of dynamic facial expressions, compared with dynamic mosaics, elicited stronger activity in the bilateral posterior cortices, including the inferior occipital gyri, fusiform gyri, and superior temporal sulci. The dynamic facial expressions also activated bilateral limbic regions, including the amygdalae and ventromedial prefrontal cortices, more strongly versus mosaics. In the same manner, activation was found in the right inferior frontal gyrus (IFG) and left cerebellum. Laterality analyses comparing original and flipped images revealed right hemispheric dominance in the superior temporal sulcus and IFG and left hemispheric dominance in the cerebellum. These results indicated that the neural mechanisms underlying processing of dynamic facial expressions include widespread social brain regions associated with perceptual, emotional, and motor functions, and include a clearly lateralized (right cortical and left cerebellar) network like that involved in language processing

Speech-evoked activation in adult temporal cortex measured using functional near-infrared spectroscopy (fNIRS): Are the measurements reliable?

Functional near-infrared spectroscopy (fNIRS) is a silent, non-invasive neuroimaging technique that is potentially well suited to auditory research. However, the reliability of auditory-evoked activation measured using fNIRS is largely unknown. The present study investigated the test-retest reliability of speech-evoked fNIRS responses in normally-hearing adults. Seventeen participants underwent fNIRS imaging in two sessions separated by three months. In a block design, participants were presented with auditory speech, visual speech (silent speechreading), and audiovisual speech conditions. Optode arrays were placed bilaterally over the temporal lobes, targeting auditory brain regions. A range of established metrics was used to quantify the reproducibility of cortical activation patterns, as well as the amplitude and time course of the haemodynamic response within predefined regions of interest. The use of a signal processing algorithm designed to reduce the influence of systemic physiological signals was found to be crucial to achieving reliable detection of significant activation at the group level. For auditory speech (with or without visual cues), reliability was good to excellent at the group level, but highly variable among individuals. Temporal-lobe activation in response to visual speech was less reliable, especially in the right hemisphere. Consistent with previous reports, fNIRS reliability was improved by averaging across a small number of channels overlying a cortical region of interest. Overall, the present results confirm that fNIRS can measure speech-evoked auditory responses in adults that are highly reliable at the group level, and indicate that signal processing to reduce physiological noise may substantially improve the reliability of fNIRS measurements

Connectivity in the human brain dissociates entropy and complexity of auditory inputs ☆

Complex systems are described according to two central dimensions: (a) the randomness of their output, quantified via entropy; and (b) their complexity, which reflects the organization of a system's generators. Whereas some approaches hold that complexity can be reduced to uncertainty or entropy, an axiom of complexity science is that signals with very high or very low entropy are generated by relatively non-complex systems, while complex systems typically generate outputs with entropy peaking between these two extremes. In understanding their environment, individuals would benefit from coding for both input entropy and complexity; entropy indexes uncertainty and can inform probabilistic coding strategies, whereas complexity reflects a concise and abstract representation of the underlying environmental configuration, which can serve independent purposes, e.g., as a template for generalization and rapid comparisons between environments. Using functional neuroimaging, we demonstrate that, in response to passively processed auditory inputs, functional integration patterns in the human brain track both the entropy and complexity of the auditory signal. Connectivity between several brain regions scaled monotonically with input entropy, suggesting sensitivity to uncertainty, whereas connectivity between other regions tracked entropy in a convex manner consistent with sensitivity to input complexity. These findings suggest that the human brain simultaneously tracks the uncertainty of sensory data and effectively models their environmental generators. Introduction Theoretical and experimental work in the fields of psychology and complexity science has arrived at two separate approaches for describing how stimuli may be encoded and what constitutes a complex stimulus (see On the other hand, the second, more recent view (e.g., Crutchfield, 2012) holds that simplicity/complexity depends on how demanding it is to model the underlying system that generated a particular stimulus or signal via the interactions of its states. From this perspective, there is a convex, inverse U-shaped relation between disorder and complexity. This is because highly ordered and highly disordered signals are typically generated by succinct, easily describable systems, whereas more sophisticated, or complex, systems generally convey intermediate levels of entropy. 1 Note that in this latter approach, complexity does not capture how difficult it is to veridically encode or reproduce any specific stimulus or signal, but rather how computationally demanding it is to model the system or source generating that signal. As can be appreciated, the two views described above are independent, and graphs depicting 1 For instance, ABCDABCD can be thought of as generated by a system (e.g., a transition matrix) that transitions between four states deterministically (a simple explanation), while a random stimulus can be characterized by a system where all state transitions are equally likely (a similarly simple explanation). http://d