Realization of Analog Wavelet Filter using Hybrid Genetic Algorithm for On-line Epileptic Event Detection

© 2020 The Author(s). This open access work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.As the evolution of traditional electroencephalogram (EEG) monitoring unit for epilepsy diagnosis, wearable ambulatory EEG (WAEEG) system transmits EEG data wirelessly, and can be made miniaturized, discrete and social acceptable. To prolong the battery lifetime, analog wavelet filter is used for epileptic event detection in WAEEG system to achieve on-line data reduction. For mapping continuous wavelet transform to analog filter implementation with low-power consumption and high approximation accuracy, this paper proposes a novel approximation method to construct the wavelet base in analog domain, in which the approximation process in frequency domain is considered as an optimization problem by building a mathematical model with only one term in the numerator. The hybrid genetic algorithm consisting of genetic algorithm and quasi-Newton method is employed to find the globally optimum solution, taking required stability into account. Experiment results show that the proposed method can give a stable analog wavelet base with simple structure and higher approximation accuracy compared with existing method, leading to a better spike detection accuracy. The fourth-order Marr wavelet filter is designed as an example using Gm-C filter structure based on LC ladder simulation, whose power consumption is only 33.4 pW at 2.1Hz. Simulation results show that the design method can be used to facilitate low power and small volume implementation of on-line epileptic event detector.Peer reviewe

A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

Multi Stage based Time Series Analysis of User Activity on Touch Sensitive Surfaces in Highly Noise Susceptible Environments

This article proposes a multistage framework for time series analysis of user activity on touch sensitive surfaces in noisy environments. Here multiple methods are put together in multi stage framework; including moving average, moving median, linear regression, kernel density estimation, partial differential equations and Kalman filter. The proposed three stage filter consisting of partial differential equation based denoising, Kalman filter and moving average method provides ~25% better noise reduction than other methods according to Mean Squared Error (MSE) criterion in highly noise susceptible environments. Apart from synthetic data, we also obtained real world data like hand writing, finger/stylus drags etc. on touch screens in the presence of high noise such as unauthorized charger noise or display noise and validated our algorithms. Furthermore, the proposed algorithm performs qualitatively better than the existing solutions for touch panels of the high end hand held devices available in the consumer electronics market qualitatively.Comment: 9 pages (including 9 figures and 3 tables); International Journal of Computer Applications (published

The cryomechanical design of MUSIC: a novel imaging instrument for millimeter-wave astrophysics at the Caltech Submillimeter Observatory

MUSIC (Multicolor Submillimeter kinetic Inductance Camera) is a new facility instrument for the Caltech Submillimeter Observatory (Mauna Kea, Hawaii) developed as a collaborative effect of Caltech, JPL, the University of Colorado at Boulder and UC Santa Barbara, and is due for initial commissioning in early 2011. MUSIC utilizes a new class of superconducting photon detectors known as microwave kinetic inductance detectors (MKIDs), an emergent technology that offers considerable advantages over current types of detectors for submillimeter and millimeter direct detection. MUSIC will operate a focal plane of 576 spatial pixels, where each pixel is a slot line antenna coupled to multiple detectors through on-chip, lumped-element filters, allowing simultaneously imaging in four bands at 0.86, 1.02, 1.33 and 2.00 mm. The MUSIC instrument is designed for closed-cycle operation, combining a pulse tube cooler with a two-stage Helium-3 adsorption refrigerator, providing a focal plane temperature of 0.25 K with intermediate temperature stages at approximately 50, 4 and 0.4 K for buffering heat loads and heat sinking of optical filters. Detector readout is achieved using semi-rigid coaxial cables from room temperature to the focal plane, with cryogenic HEMT amplifiers operating at 4 K. Several hundred detectors may be multiplexed in frequency space through one signal line and amplifier. This paper discusses the design of the instrument cryogenic hardware, including a number of features unique to the implementation of superconducting detectors. Predicted performance data for the instrument system will also be presented and discussed

A 13-bit, 2.2-MS/s, 55-mW multibit cascade ΣΔ modulator in CMOS 0.7-μm single-poly technology

This paper presents a CMOS 0.7-μm ΣΔ modulator IC that achieves 13-bit dynamic range at 2.2 MS/s with an oversampling ratio of 16. It uses fully differential switched-capacitor circuits with a clock frequency of 35.2 MHz, and has a power consumption of 55 mW. Such a low oversampling ratio has been achieved through the combined usage of fourth-order filtering and multibit quantization. To guarantee stable operation for any input signal and/or initial condition, the fourth-order shaping function has been realized using a cascade architecture with three stages; the first stage is a second-order modulator, while the others are first-order modulators - referred to as a 2-1-1mb architecture. The quantizer of the last stage is 3 bits, while the other quantizers are single bit. The modulator architecture and coefficients have been optimized for reduced sensitivity to the errors in the 3-bit quantization process. Specifically, the 3-bit digital-to-analog converter tolerates 2.8% FS nonlinearity without significant degradation of the modulator performance. This makes the use of digital calibration unnecessary, which is a key point for reduced power consumption. We show that, for a given oversampling ratio and in the presence of 0.5% mismatch, the proposed modulator obtains a larger signal-to-noise-plus-distortion ratio than previous multibit cascade architectures. On the other hand, as compared to a 2-1-1single-bit modulator previously designed for a mixed-signal asymmetrical digital subscriber line modem in the same technology, the modulator in this paper obtains one more bit resolution, enhances the operating frequency by a factor of two, and reduces the power consumption by a factor of four.Comisión Interministerial de Ciencia y Tecnología TIC97-0580European Commission ESPRIT 879

Efficient spiking neural network model of pattern motion selectivity in visual cortex

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction- selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40∈×∈40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available. © 2014 Springer Science+Business Media New York