A low-noise low-power noise-adaptive neural amplifier in 0.13um CMOS technology

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and in elucidating human neurophysiology. The advent of multichannel microelectrode arrays has driven the need for electronics to record neural signals from many neurons. The dynamic range of the system is limited by background system noise which varies over time. We propose a neural amplifier in UMC 130 nm, 2P8M CMOS technology. It can be biased adaptively from 200 nA to 2 uA, modulating input referred noise from 9.92 uV to 3.9 uV. We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. The amplifier can pass signal from 5 Hz to 7 kHz while rejecting input DC offsets at electrode-electrolyte interface. The bandwidth of the amplifier can be tuned by the pseudo-resistor for selectively recording low field potentials (LFP) or extra cellular action potentials (EAP). The amplifier achieves a mid-band voltage gain of 37 dB and minimizes the attenuation of the signal from neuron to the gate of the input transistor. It is used in fully differential configuration to reject noise of bias circuitry and to achieve high PSRR.

On Selection of Search Space Dimension in Compressive Sampling Matching Pursuit

Compressive Sampling Matching Pursuit (CoSaMP) is one of the popular greedy methods in the emerging field of Compressed Sensing (CS). In addition to the appealing empirical performance, CoSaMP has also splendid theoretical guarantees for convergence. In this paper, we propose a modification in CoSaMP to adaptively choose the dimension of search space in each iteration, using a threshold based approach. Using Monte Carlo simulations, we show that this modification improves the reconstruction capability of the CoSaMP algorithm in clean as well as noisy measurement cases. From empirical observations, we also propose an optimum value for the threshold to use in applications.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system’s Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.

Experiments on thermal response of low aspect ratio packed beds at high Reynolds numbers with varying inflow temperature

An experimental study for transient temperature response of low aspect ratio packed beds at high Reynolds numbers for a free stream with varying inlet temperature is presented. The packed bed is used as a compact heat exchanger along with a solid propellant gas-generator, to generate room temperature gases for use in applications such as control actuation and air bottle pressurization. Packed beds of lengths similar to 200 mm and 300 mm were characterized for packing diameter based Reynolds numbers, Re-d ranging from 0.6 x 10(4) to 8.5 x 10(4). The solid packing used in the bed consisted of circle divide 9.5 mm and circle divide 5 mm steel spheres with suitable arrangements to eliminate flow entrance and exit effects. The ratios of packed bed diameter to packing diameter for 9.5 mm and 5 mm sphere packing were similar to 9.5 and 18 respectively, with the average packed bed porosities around 0.4. Gas temperatures were measured at the entry, exit and at three axial locations along centre-line in the packed beds. The solid packing temperature was measured at three axial locations in the packed bed. An average Nusselt number correlation of the form Nu(d) = 3.91Re(d)(05) for Re-d range of 10(4) is proposed. For engineering applications of packed beds such as pebble bed heaters, thermal storage systems, and compact heat exchangers a simple procedure is suggested for calculating unsteady gas temperature at packed bed exit for packing Biot number Bi-d < 0.1. (C) 2012 Elsevier Inc. All rights reserved.

Low temperature growth of SnO2 nanowires by electron beam evaporation and their application in UV light detection

For the first time, high quality tin oxide (SnO2) nanowires have been synthesized at a low substrate temperature of 450 degrees C via vapor-liquid-solid mechanism using an electron beam evaporation technique. The grown nanowires have shown length of 2-4 mu m and diameter of 20-60 nm. High resolution transmission electron microscope studies on the grown nanowires have shown the single crystalline nature of the SnO2 nanowires. We investigated the effect of growth temperature and oxygen partial pressure on SnO2 nanowires growth. Variation of substrate temperature at a constant oxygen partial pressure of 4 x 10(-4) mbar suggested that a temperature equal to or greater than 450 degrees C was the best condition for phase pure SnO2 nanowires growth. The SnO2 nanowires grown on a SiO2 substrate were subjected to UV photo detection. The responsivity and quantum efficiency of SnO2 NWs photo detector (at 10V applied bias) was 12 A/W and 45, respectively, for 12 mu W/cm(2) UV lamp (330 nm) intensity on the photo detector.

Charge transport and magnetic properties of coaxial composite fibrils of polypyrrole/multiwall carbon nanotubes at low temperature

We report the low temperature electrical and magnetic properties of polypyrrole (PPy)/multiwall carbon nanotube (MWNT) coaxial composite fibrils synthesized by the electro-polymerization method. The iron-filled MWNTs were first grown by chemical vapor deposition of a mixture of liquid phase organic compound and ferrocene by the one step method. Then the PPy/MWNT fibrils were prepared by the electrochemical polymerization process. Electron microscopy studies reveal that PPy coating on the surface of nanotube is quite uniform throughout the length. The temperature dependent electrical resistivity and magnetization measurements were done from room temperature down to 5 and 10 K, respectively. The room temperature resistivity (rho) of PPy/MWNT composite fibril sample is similar to 3.8 Omega m with resistivity ratio R-5 K/R-300 K] of similar to 300, and the analysis of rho(T) in terms of reduced activation energy shows that resistivity lies in the insulating regime below 40 K. The resistivity varies according to three dimensional variable range hopping mechanism at low temperature. The magnetization versus applied field (M-H loop) data up to a field of 20 kOe are presented, displaying ferromagnetic behavior at all temperatures with enhanced coercivities similar to 680 and 1870 Oe at room temperature and 10 K, respectively. The observation of enhanced coercivity is due to significant dipolar interaction among encapsulated iron nanoparticles, and their shape anisotropy contribution as well.

Hybrid online non-negative matrix factorization for clustering of documents

When document corpus is very large, we often need to reduce the number of features. But it is not possible to apply conventional Non-negative Matrix Factorization(NMF) on billion by million matrix as the matrix may not fit in memory. Here we present novel Online NMF algorithm. Using Online NMF, we reduced original high-dimensional space to low-dimensional space. Then we cluster all the documents in reduced dimension using k-means algorithm. We experimentally show that by processing small subsets of documents we will be able to achieve good performance. The method proposed outperforms existing algorithms.

Performance improvement of short-length regular low-density parity-check codes with low-complexity post-processing

It is well known that extremely long low-density parity-check (LDPC) codes perform exceptionally well for error correction applications, short-length codes are preferable in practical applications. However, short-length LDPC codes suffer from performance degradation owing to graph-based impairments such as short cycles, trapping sets and stopping sets and so on in the bipartite graph of the LDPC matrix. In particular, performance degradation at moderate to high E-b/N-0 is caused by the oscillations in bit node a posteriori probabilities induced by short cycles and trapping sets in bipartite graphs. In this study, a computationally efficient algorithm is proposed to improve the performance of short-length LDPC codes at moderate to high E-b/N-0. This algorithm makes use of the information generated by the belief propagation (BP) algorithm in previous iterations before a decoding failure occurs. Using this information, a reliability-based estimation is performed on each bit node to supplement the BP algorithm. The proposed algorithm gives an appreciable coding gain as compared with BP decoding for LDPC codes of a code rate equal to or less than 1/2 rate coding. The coding gains are modest to significant in the case of optimised (for bipartite graph conditioning) regular LDPC codes, whereas the coding gains are huge in the case of unoptimised codes. Hence, this algorithm is useful for relaxing some stringent constraints on the graphical structure of the LDPC code and for developing hardware-friendly designs.