Blind separation of multiple co-channel BPSK signals arriving at an antenna array

In this letter, we propose a method for blind separation of d co-channel BPSK signals arriving at an antenna array. Our method involves two steps. In the first step, the received data vectors at the output of the array is grouped into 2d clusters. In the second step, we assign the 2d d-tuples with ±1 elements to these clusters in a consistent fashion. From the knowledge of the cluster to which a data vector belongs, we estimate the bits transmitted at that instant. Computer simulations are used to study the performance of our method

Characteristics of j.f.e.t.s. taking the separation of gate electrode from the source and drain electrodes into account

The performance characteristics of a junction field-effect transistor (j.f.e.t.) are evaluated considering the presence of the gap between the gate electrode and the source and drain terminals. It is concluded that the effect of the gap is to demand a higher drain voltage to maintain the same drain current. So long as the device is operated at the same drain current, the presence of the gap does not change the performance of the device as an amplifier. The nature of the performance of the device as a variable resistor is not affected by the gap if it is less than or equal to the physical height of the channel. For gap lengths larger than the channel height, the effect of the gap is to add a series resistance in the drain.

Blind separation of auditory event-related brain responses into independent components

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Geochemical Assessment and Separation of Source Waters in the Upper Boulder River Watershed Near Boulder, MT

Environmental samples were collected at three surface water sites between 5/21/2011 and 11/21/2014 along the Upper Boulder River near Boulder Montana. The sites were located at Bernice (within the mountain block), near the High Ore drainage (near the mountain block/basin transition), and at the USGS Gauging Station near Boulder, Montana (within the basin). The parameters measured in the field were SC, temperature, and alkalinity with occasional pH measurements. We collected samples for anions, cations, and stable isotopes in the catchment. We identified endmembers by sampling snow and groundwater and determined from available data an approximate endmember for rain, snow, and groundwater. We used temporal and spatial variations of water chemistry and isotopes to generate an endmember mixing model. Groundwater was found to always be an important contributor to river flow and could increase by nearly an order of magnitude during large snowmelt events. This resulted in groundwater comprising ~20% of total river flow during snowmelt at all sites. At peak snowmelt we observed that near surface water contributions to the river were from a mixture of rain and snow. Soil water, though not sampled, was hypothesized to be an important part of the hydrologic story. If so, the endmember contributions determined in this study may be different. Groundwater may have the highest variation depending on water chemistry of shallow soil water.

Clustering of ad-hoc microphone arrays for robust blind beamforming

This paper proposes a clustered approach for blind beamfoming from ad-hoc microphone arrays. In such arrangements, microphone placement is arbitrary and the speaker may be close to one, all or a subset of microphones at a given time. Practical issues with such a configuration mean that some microphones might be better discarded due to poor input signal to noise ratio (SNR) or undesirable spatial aliasing effects from large inter-element spacings when beamforming. Large inter-microphone spacings may also lead to inaccuracies in delay estimation during blind beamforming. In such situations, using a cluster of microphones (ie, a sub-array), closely located both to each other and to the desired speech source, may provide more robust enhancement than the full array. This paper proposes a method for blind clustering of microphones based on the magnitude square coherence function, and evaluates the method on a database recorded using various ad-hoc microphone arrangements.

Clustered blind beamforming from ad-hoc microphone arrays

Microphone arrays have been used in various applications to capture conversations, such as in meetings and teleconferences. In many cases, the microphone and likely source locations are known \emph{a priori}, and calculating beamforming filters is therefore straightforward. In ad-hoc situations, however, when the microphones have not been systematically positioned, this information is not available and beamforming must be achieved blindly. In achieving this, a commonly neglected issue is whether it is optimal to use all of the available microphones, or only an advantageous subset of these. This paper commences by reviewing different approaches to blind beamforming, characterising them by the way they estimate the signal propagation vector and the spatial coherence of noise in the absence of prior knowledge of microphone and speaker locations. Following this, a novel clustered approach to blind beamforming is motivated and developed. Without using any prior geometrical information, microphones are first grouped into localised clusters, which are then ranked according to their relative distance from a speaker. Beamforming is then performed using either the closest microphone cluster, or a weighted combination of clusters. The clustered algorithms are compared to the full set of microphones in experiments on a database recorded on different ad-hoc array geometries. These experiments evaluate the methods in terms of signal enhancement as well as performance on a large vocabulary speech recognition task.

Development of analytical methods for the separation of plutonium, americium, curium and neptunium from environmental samples

In this work, separation methods have been developed for the analysis of anthropogenic transuranium elements plutonium, americium, curium and neptunium from environmental samples contaminated by global nuclear weapons testing and the Chernobyl accident. The analytical methods utilized in this study are based on extraction chromatography. Highly varying atmospheric plutonium isotope concentrations and activity ratios were found at both Kurchatov (Kazakhstan), near the former Semipalatinsk test site, and Sodankylä (Finland). The origin of plutonium is almost impossible to identify at Kurchatov, since hundreds of nuclear tests were performed at the Semipalatinsk test site. In Sodankylä, plutonium in the surface air originated from nuclear weapons testing, conducted mostly by USSR and USA before the sampling year 1963. The variation in americium, curium and neptunium concentrations was great as well in peat samples collected in southern and central Finland in 1986 immediately after the Chernobyl accident. The main source of transuranium contamination in peats was from global nuclear test fallout, although there are wide regional differences in the fraction of Chernobyl-originated activity (of the total activity) for americium, curium and neptunium.