Local polynomial modelling of time-varying autoregressive processes and its application to the analysis of event-related electroencephalogram

This paper proposes a new method for identification of time-varying autoregressive (TVAR) models based on local polynomial modeling (LPM) and applies it to investigate the dynamic spectral information of event-related electroencephalogram (EEG). The proposed method models the TVAR coefficients locally by polynomials and estimates those using least-squares estimation with a kernel having a certain bandwidth. A data-driven variable bandwidth selection method is developed to obtain the optimal bandwidth, which minimizes the mean squared error (MSE). Simulation results show that the LPM-based TVAR identification method outperforms conventional methods for different scenarios. The advantages of the LPM method make it a useful high-resolution timefrequency analysis (TFA) technique for nonstationary biomedical signals like EEG. Experimental results show that the LPM method can reveal more meaningful time-frequency characteristics than wavelet transform. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems (ISCAS 2010), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 3124-312

A subspace-based method for DOA estimation of uniform linear array in the presence of mutual coupling

This paper develops a subspace-based method for direction-of-arrival (DOA) estimation of uniform linear array (ULA) in the presence of mutual coupling. As the mutual coupling coefficient between two sensor elements is inversely related to their separation and is negligible when they are separated by a few wavelengths, the mutual coupling matrix (MCM) of a ULA can be well approximated as a banded symmetric Toeplitz matrix, which greatly reduces the number of unknown parameters to be estimated. Using the subspace principle, we propose a new method for joint estimation of the DOAs of incoming signals and banded symmetric Toeplitz MCM by reconstructing the steering vector to a specific matrix form. The proposed method achieves a better performance especially for weak signals than the method in [8], since the whole array, instead of the middle subarray in [8], is used for DOA estimation. Simulation results illustrate that both DOAs and mutual coupling coefficients can be estimated efficiently with the proposed method. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems (ISCAS) 2010, Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 1879-188

Modern Methods of Time-Frequency Warping of Sound Signals

Tato práce se zabývá reprezentací nestacionárních harmonických signálů s časově proměnnými komponentami. Primárně je zaměřena na Harmonickou transformaci a jeji variantu se subkvadratickou výpočetní složitostí, Rychlou harmonickou transformaci. V této práci jsou prezentovány dva algoritmy využívající Rychlou harmonickou transformaci. Prvni používá jako metodu odhadu změny základního kmitočtu sbírané logaritmické spektrum a druhá používá metodu analýzy syntézou. Oba algoritmy jsou použity k analýze řečového segmentu pro porovnání vystupů. Nakonec je algoritmus využívající metody analýzy syntézou použit na reálné zvukové signály, aby bylo možné změřit zlepšení reprezentace kmitočtově modulovaných signálů za použití Harmonické transformace.This thesis deals with representation of non-stationary harmonic signals with time-varying components. Its main focus is aimed at Harmonic Transform and its variant with subquadratic computational complexity, the Fast Harmonic Transform. Two algorithms using the Fast Harmonic Transform are presented. The first uses the gathered log-spectrum as fundamental frequency change estimation method, the second uses analysis-by-synthesis approach. Both algorithms are used on a speech segment to compare its output. Further the analysis-by-synthesis algorithm is applied on several real sound signals to measure the increase in the ability to represent real frequency-modulated signals using the Harmonic Transform.

Single-input Multiple-output Tunable Log-domain Current-mode Universal Filter

This paper describes the design of a current-mode single-input multiple-output (SIMO) universal filter based on the log-domain filtering concept. The circuit is a direct realization of a first-order differential equation for obtaining the lossy integrator circuit. Lossless integrators are realized by log-domain lossy integrators. The proposed filter comprises only two grounded capacitors and twenty-four transistors. This filter suits to operate in very high frequency (VHF) applications. The pole-frequency of the proposed filter can be controlled over five decade frequency range through bias currents. The pole-Q can be independently controlled with the pole-frequency. Non-ideal effects on the filter are studied in detail. A validated BJT model is used in the simulations operated by a single power supply, as low as 2.5 V. The simulation results using PSpice are included to confirm the good performances and are in agreement with the theory

Wireless neural/EMG telemetry system for freely moving insects

Journal ArticleWe have developed a miniature telemetry system that captures neural, EMG, and acceleration signals from a freely moving insect and transmits the data wirelessly to a remote digital receiver. The system is based on a custom low-power integrated circuit that amplifies and digitizes four biopotential signals as well as three acceleration signals from an off-chip MEMS accelerometer, and transmits this information over a wireless 920-MHz telemetry link. The unit weighs 0.79 g and runs for two hours on two small batteries. We have used this system to monitor neural and EMG signals in jumping and flying locusts

Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems

Chicca E, Stefanini F, Bartolozzi C, Indiveri G. Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems. In: Proceedings of the IEEE. Proceedings of the IEEE. Vol 102. Piscataway, NJ: IEEE; 2014: 1367-1388.Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them; we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition

Discriminating multiple JPEG compression using first digit features

The analysis of JPEG double-compressed images is a problem largely studied by the multimedia forensics community, as it might be exploited, e.g., for tampering localization or source device identification. In many practical scenarios, like photos uploaded on blogs, on-line albums, and photo sharing web sites, images might be JPEG compressed several times. However, the identification of the number of compression stages applied to an image remains an open issue. We proposes a forensic method based on the analysis of the distribution of the first significant digits of the discrete cosine transform coefficients, which follow Benford's law in images compressed just once. Then, the detector is optimized and extended in order to identify accurately the number of compression stages applied to an image. The experimental validation considers up to four consecutive compression stages and shows that the proposed approach extends and outperforms the previously-published algorithms for double JPEG compression detection

Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG

This paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal. © 2006 IEEE.published_or_final_versio

Modeling Memristive Biosensors

In the present work, a computational study is carried out investigating the relationship between the biosensing and the electrical characteristics of two-terminal Schottky- barrier silicon nanowire devices. The model suggested successfully reproduces computationally the experimentally obtained electrical behavior of the devices prior to and after the surface bio-modification. Throughout modeling and simulations, it is confirmed that the nanofabricated devices present electrical behavior fully equivalent to that of a memristor device, according to literature. Furthermore, the model introduced successfully reproduces computationally the voltage gap appearing in the current to voltage characteristics for nanowire devices with bio- modified surface. Overall, the present study confirms the implication of the memristive effect for bio sensing applications, therefore demonstrating the Memristive Biosensors