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.

A Wavelet based Teager Energy Operator for Spike Detection in Microelectrode Array Recordings

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the `local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike. In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90%and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

3D shape extraction of human face in presence of facial hair: A profilometric approach

The increasing use of 3D modeling of Human Face in Face Recognition systems, User Interfaces, Graphics, Gaming and the like has made it an area of active study. Majority of the 3D sensors rely on color coded light projection for 3D estimation. Such systems fail to generate any response in regions covered by Facial Hair (like beard, mustache), and hence generate holes in the model which have to be filled manually later on. We propose the use of wavelet transform based analysis to extract the 3D model of Human Faces from a sinusoidal white light fringe projected image. Our method requires only a single image as input. The method is robust to texture variations on the face due to space-frequency localization property of the wavelet transform. It can generate models to pixel level refinement as the phase is estimated for each pixel by a continuous wavelet transform. In cases of sparse Facial Hair, the shape distortions due to hairs can be filtered out, yielding an estimate for the underlying face. We use a low-pass filtering approach to estimate the face texture from the same image. We demonstrate the method on several Human Faces both with and without Facial Hairs. Unseen views of the face are generated by texture mapping on different rotations of the obtained 3D structure. To the best of our knowledge, this is the first attempt to estimate 3D for Human Faces in presence of Facial hair structures like beard and mustache without generating holes in those areas.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Nonlinear fracture properties of concrete-concrete interfaces

The fracture properties of different concrete-concrete interfaces are determined using the Bazant's size effect model. The size effect on fracture properties are analyzed using the boundary effect model proposed by Wittmann and his co-workers. The interface properties at micro-level are analyzed through depth sensing micro-indentation and scanning electron microscopy. Geometrically similar beam specimens of different sizes having a transverse interface between two different strengths of concrete are tested under three-point bending in a closed loop servo-controlled machine with crack mouth opening displacement control. The fracture properties such as, fracture energy (G(f)), length of process zone (c(f)), brittleness number (beta), critical mode I stress intensity factor (K-ic), critical crack tip opening displacement CTODc (delta(c)), transitional ligament length to free boundary (a(j)), crack growth resistance curve and micro-hardness are determined. It is seen that the above fracture properties decrease as the difference between the compressive strength of concrete on either side of the interface increases. (C) 2010 Elsevier Ltd. All rights reserved.

Fracture Properties of Concrete-Concrete Interfaces Using Digital Image Correlation

The mode I and mode II fracture toughness and the critical strain energy release rate for different concrete-concrete jointed interfaces are experimentally determined using the Digital Image Correlation technique. Concrete beams having different compressive strength materials on either side of a centrally placed vertical interface are prepared and tested under three-point bending in a closed loop servo-controlled testing machine under crack mouth opening displacement control. Digital images are captured before loading (undeformed state) and at different instances of loading. These images are analyzed using correlation techniques to compute the surface displacements, strain components, crack opening and sliding displacements, load-point displacement, crack length and crack tip location. It is seen that the CMOD and vertical load-point displacement computed using DIC analysis matches well with those measured experimentally.

A stylometric study and assessment of machine translators

In this work we present a statistical approach inspired by stylometry -measurement of author style- to study the characteristics of machine translators. Our approach quantifies the style of a translator in terms of the properties derived from the distribution of stopwords in its output - a standard approach in modern stylometry. Our study enables us to match translated text to the source machine translator that generated them. Also, the stylometric closeness of human generated text to that generated by machine translators provides handles to assess the quality of machine translators.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Interplay of Substrate Conductivity, Cellular Microenvironment, and Pulsatile Electrical Stimulation toward Osteogenesis of Human Mesenchymal Stem Cells in Vitro

The influences of physical stimuli such as surface elasticity, topography, and chemistry over mesenchymal stem cell proliferation and differentiation are well investigated. In this context, a fundamentally different approach was adopted, and we have demonstrated the interplay of inherent substrate conductivity, defined chemical composition of cellular microenvironment, and intermittent delivery of electric pulses to drive mesenchymal stem cell differentiation toward osteogenesis. For this, conducting polyaniline (PANI) substrates were coated with collagen type 1 (Coll) alone or in association with sulfated hyaluronan (sHya) to form artificial extracellular matrix (aECM), which mimics the native microenvironment of bone tissue. Further, bone marrow derived human mesenchymal stem cells (hMSCs) were cultured on these moderately conductive (10(-4)10(-3) S/cm) aECM coated PANI substrates and exposed intermittently to pulsed electric field (PEF) generated through transformer-like coupling (TLC) approach over 28 days. On the basis of critical analysis over an array of end points, it was inferred that Coll/sHya coated PANI (PANI/Coll/sHya) substrates had enhanced proliferative capacity of hMSCs up to 28 days in culture, even in the absence of PEF stimulation. On the contrary, the adopted PEF stimulation protocol (7 ms rectangular pulses, 3.6 mV/cm, 10 Hz) is shown to enhance osteogenic differentiation potential of hMSCs. Additionally, PEF stimulated hMSCs had also displayed different morphological characteristics as their nonstimulated counterparts. Concomitantly, earlier onset of ALP activity was also observed on PANI/Coll/sHya substrates and resulted in more calcium deposition. Moreover, real-time polymerase chain reaction results indicated higher mRNA levels of alkaline phosphatase and osteocalcin, whereas the expression of other osteogenic markers such as Runt-related transcription factor 2, Col1A, and osteopontin exhibited a dynamic pattern similar to control cells that are cultured in osteogenic medium. Taken together, our experimental results illustrate the interplay of multiple parameters such as substrate conductivity, electric field stimulation, and aECM coating on the modulation of hMSC proliferation and differentiation in vitro.

Combined Effect of Cryogel Matrix and Temperature-Reversible Soluble-Insoluble Polymer for the Development of in Vitro Human Liver Tissue

Hepatic cell culture on a three-dimensional (3D) matrix or as a hepatosphere appears to be a promising in vitro biomimetic system for liver tissue engineering applications. In this study, we have combined the concept of a 3D scaffold and a spheroid culture to develop an in vitro model to engineer liver tissue for drug screening. We have evaluated the potential of poly(ethylene glycol)-alginate-gelatin (PAG) cryogel matrix for in vitro culture of human liver cell lines. The synthesized cryogel matrix has a flow rate of 7 mL/min and water uptake capacity of 94% that enables easy nutrient transportation in the in vitro cell culture. Youngs modulus of 2.4 kPa and viscoelastic property determine the soft and elastic nature of synthesized cryogel. Biocompatibility of PAG cryogel was evaluated through MTT assay of HepG2 and Huh-7 cells on matrices. The proliferation and functionality of the liver cells were enhanced by culturing hepatic cells as spheroids (hepatospheres) on the PAG cryogel using temperature-reversible soluble-insoluble polymer, poly(N-isopropylacrylamide) (PNIPAAm). Pore size of the cryogel above 100 mu m modulated spheroid size that can prevent hypoxia condition within the spheroid culture. Both the hepatic cells have shown a significant difference (P < 0.05) in terms of cell number and functionality when cultured with PNIPAAm. After 10 days of culture using 0.05% PNIPAAm, the cell number increased by 11- and 7-fold in case of HepG2 and Huh-7 cells, respectively. Similarly, after 10 days of hepatic spheroids culture on PAG cryogel, the albumin production, urea secretion, and CYP450 activity were significantly higher in case of culture with PNIPAAm. The developed tissue mass on the PAG cryogel in the presence of PNIPAAm possess polarity, which was confirmed using F-actin staining and by presence of intercellular bile canalicular lumen. The developed cryogel matrix supports liver cells proliferation and functionality and therefore can be used for in vitro and in vivo drug testing.