Communication channel analysis and real time compressed sensing for high density neural recording devices

Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

Phase Synchronization Operator for On-Chip Brain Functional Connectivity Computation

This paper presents an integer-based digital processor for the calculation of phase synchronization between two neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters, and adders. The processor, fabricated in a 0.18- μ m CMOS process, only occupies 0.05 mm 2 and consumes 15 nW from a 0.5 V supply voltage at a signal input rate of 1024 S/s. These low-area and low-power features make the proposed processor a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for assessing the patterns of correlated activity in neural assemblies through the evaluation of functional connectivity maps.Ministerio de Economía y Competitividad TEC2016-80923-POffice of Naval Research (USA) N00014-19-1-215

Time domain analysis of switching transient fields in high voltage substations

Switching operations of circuit breakers and disconnect switches generate transient currents propagating along the substation busbars. At the moment of switching, the busbars temporarily acts as antennae radiating transient electromagnetic fields within the substations. The radiated fields may interfere and disrupt normal operations of electronic equipment used within the substation for measurement, control and communication purposes. Hence there is the need to fully characterise the substation electromagnetic environment as early as the design stage of substation planning and operation to ensure safe operations of the electronic equipment. This paper deals with the computation of transient electromagnetic fields due to switching within a high voltage air-insulated substation (AIS) using the finite difference time domain (FDTD) metho

Otkrivanje pogreške u analognim sklopovima analizom relativne amplitude i faze

A new method for detection of parametric faults occurring in analog circuits based on relative amplitude and relative phase analysis of the Circuit Under Test (CUT) is proposed. The relative amplitude is the common power change of the signals and the relative phase presents the relative phase offset of the signals. In the proposed method, the value of each component of the CUT is varied within its tolerance limit using Monte Carlo simulation. The upper and lower bounds of relative amplitude and phase of the CUT sampling series are obtained. While testing, the relative amplitude and phase value of the analog circuit are obtained. If any one of the relative amplitude and phase values exceed the bounds then the CUT is declared faulty. The effectiveness of the proposed method is validated through HSpice/MATLAB simulations of two benchmark circuits and the practical circuit test of Tow-Thomas circuit.U ovome članku predložena je nova metoda otkrivanja parametarskih pogrešaka u analognim sklopovima temeljena na analizi relativne amplitude i faze promatranog sklopa (eng. Circuit Under Test, CUT). Relativna amplituda predstavlja zajedničku promjenu snage signala, dok relativna faza predstavlja pomak u fazi među signalima. U predloženoj metodi, koristeći Monte Carlo simulacije, vrijednost svake komponente CUT-a mijenja se unutar svojih granica tolerancije. Na taj način dobivaju se gornja i donja granica relativne amplitude i faze CUT uzoraka, dok se sama relativna amplituda i faza dobivaju tijekom testiranja. U slučaju da ijedan od tih dvaju faktora prelazi granicu, CUT se proglašava neispravnim. Učinkovitost predložene metode ispitana je pomoću HSpice/MATLAB simulacija nad dva referentna sklopa te na Tow-Thomas sklopu

Measurements, Models, Systems and Design

531 s.

Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring

A general framework for regression modeling using localized frequency characteristics of explanatory variables is proposed. This novel framework can be used in any application where the aim is to model an evolving process sequentially based on multiple time series data. Furthermore, this framework allows time series to be transformed and combined to simultaneously boost important characteristics and reduce noise. A wavelet transform is used to isolate key frequency structure and perform data reduction. The method is highly adaptive, since wavelets are effective at extracting localized information from noisy data. This adaptivity allows rapid identification of changes in the evolving process. Finally, a regression model uses functions of the wavelet coefficients to classify the evolving process into one of a set of states which can then be used for automatic monitoring of the system. As motivation and illustration, industrial process monitoring using electrical tomography measurements is considered. This technique provides useful data without intruding into the industrial process. Statistics derived from the wavelet transform of the tomographic data can be enormously helpful in monitoring and controlling the process. The predictive power of the proposed approach is explored using real and simulated tomographic data. In both cases, the resulting models successfully classify different flow regimes and hence provide the basis for reliable online monitoring and control of industrial processes