A pulsed diagonal-beam ultrasonic airflow meter.

The construction and specific function of a new ultrasonic flowmeter are described. The mean velocity of the respiratory airflow is calculated by measuring the transit times of short ultrasonic pulse trains, simultaneously transmitted upstream and downstream at a 500-Hz rate. The flowmeter system consists of a control unit and a separate flow head. The former includes the power supplies, a controlling microprocessor, most of the signal-processing circuitry, and three analog outputs for flow, volume, and temperature. The flow head contains the respiratory tube with a constant circular cross section (length 90 mm, diam 20 mm, dead space 35 ml), a fast temperature sensor, two electronic circuits for processing of flow and temperature data, and a sound transmission channel with two capacitive ultrasonic wide-band transducers. This respiratory airflow meter, suitable for spirometric maneuvers (vital capacity, forced vital capacity) as well as for long-term breath-by-breath respiratory analysis, is extremely fast (response time 1-2 ms) and accurate (volume accuracy with room air +/- 0.7%), with low noise (below 9 ml/s), a wide flow range (bidirectional from 0 to 9 l/s), and a flat frequency response up to 70 Hz.

Analog CMOS implementation of a multilayer perceptron with nonlinear synapses.

A neurocomputer based on a high-density analog integrated circuit developed in a 3 mum CMOS technology has been built. The 1.6 mmx2.4 mm chip contains 18 neurons and 161 synapses in three layers, and provides 16 inputs and 4 outputs. The weights are stored on storage capacitors of the synapses. A formalization of the error back-propagation algorithm which allows the use of very small nonlinear synapses is shown. The influence of offset voltages in the synapses on the circuit performance is analyzed. Some experimental results are reported and discussed.

Fast neural net simulation with a DSP processor array.

This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC (multiprocessor system with intelligent communication), is operational and runs the backpropagation algorithm at a speed of 330 million connection updates per second (continuous weight update) using 32-b floating-point precision. This is equal to 1.4 Gflops sustained performance. The complete system with 3.8 Gflops peak performance consumes less than 800 W of electrical power and fits into a 19-in rack. While reaching the speed of modern supercomputers, MUSIC still can be used as a personal desktop computer at a researcher's own disposal. In neural net simulation, this gives a computing performance to a single user which was unthinkable before. The system's real-time interfaces make it especially useful for embedded applications.

All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions.

EEG signals have been considered to be generated either by stochastic processes or by non-linear deterministic systems exhibiting chaotic behavior. To address this problem, the correlation dimension of the EEG was computed and compared to the correlation dimension of an artificial signal with identical power spectrum. By using a new type of personal super computer we were able for the first time to calculate the correlation dimension for the sleep episode of an entire night as well as for the corresponding artificial signal. The correlation dimension was high in episodes of rapid eye movement (REM) sleep, declined progressively within each non-REM sleep episode, and reached a low level at times when EEG slow waves (0.75-4.5 Hz) were dominant. The correlation dimension of the artificial signal and the EEG changed largely in parallel, although on average the values of the artificial signal were 7.3% higher. These results do not support the hypothesis that the sleep EEG is generated by a chaotic attractor.

Correlation dimension of the human sleep electroencephalogram: cyclic changes in the course of the night.

The complexity of the electroencephalogram (EEG) during human sleep can be estimated by calculating the correlation dimension. Due to the large number of calculations required by this approach, only selected short (4-164 s) segments of the sleep EEG have been analysed previously. By using a new type of personal supercomputer, we were able to calculate the correlation dimension of overlapping 1 min EEG segments for the entire sleep episode (480 min) of 11 subjects and thereby delineate the time course of the changes. The correlation dimension was high in episodes of rapid eye movement (REM) sleep, declined progressively within each non-REM sleep episode, and reached a low level at times when EEG slow waves (0.75-4.5 Hz) were dominant. However, whereas slow-wave activity showed its typical progressive decline from non-REM/REM sleep cycle 1 to 4, no such trend was present for the correlation dimension. By providing an estimate of the complexity of a signal and being independent of amplitude and frequency measures, the correlation dimension represents a novel approach to exploring the dynamics of sleep and the processes underlying its regulation.