There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Our understanding of the cellular implementation of systems-level neural processes like action, thought and emotion has been limited by the availability of tools to interrogate specific classes of neural cells within intact, living brain tissue. Here we identify and develop an archaeal light-driven chloride pump (NpHR) from Natronomonas pharaonis for temporally precise optical inhibition of neural activity. NpHR allows either knockout of single action potentials, or sustained blockade of spiking. NpHR is compatible with ChR2, the previous optical excitation technology we have described, in that the two opposing probes operate at similar light powers but with well-separated action spectra. NpHR, like ChR2, functions in mammals without exogenous cofactors, and the two probes can be integrated with calcium imaging in mammalian brain tissue for bidirectional optical modulation and readout of neural activity. Likewise, NpHR and ChR2 can be targeted together to Caenorhabditis elegans muscle and cholinergic motor neurons to control locomotion bidirectionally. NpHR and ChR2 form a complete system for multimodal, high-speed, genetically targeted, all-optical interrogation of living neural circuits.

Multimodal fast optical interrogation of neural circuitry

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

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Introduction To Robust Estimation And Hypothesis Testing

A novel temperature sensor based on rare earth fiber peanut (REFP) shape structure in fiber ring laser (FRL) is proposed and demonstrated. A single-mode rare earth doped fiber is used to fabricate a peanut structure to produce Mach–Zehnder interferometer (MZI). Benefited from the strong thermo-optic effect of rare earth ions in fiber, high sensitivity measurement of temperature is exploited to realize. More importantly, there is no need for additional filters compared to traditional fiber laser sensors. The sensitivity of refractive index (RI) was measured to further verify the sensing performance of MZI. The experimental results show that compared with the external sensing unit, the Er-doped fiber peanut (EDFP) structure has higher thermo optic effect coefficient and superior temperature sensitivity. The temperature and RI sensitivity of the EDFP is 0.158 nm/°C and −19.55 nm/RIU, respectively. The optical fiber MZI sensor based on the EDFP has the advantages of high sensitivity, good repeatability, simple fabrication, and compact structure. It has good application prospects in biomedicine, aerospace, and marine development.

In-Fiber Mach–Zehnder Interferometer Sensor Based on Er Doped Fiber Peanut Structure in Fiber Ring Laser

This paper describes a 90nm CMOS low-noise low-power biopotential signal readout front-end (RFE). The front-stage instrumentation amplifier (IA) features a chopper; an AC-coupler and a novel chopper notch filter for minimizing the dc-offset; transistors' flicker noise and 50Hz powerline interference concurrently. A noise-aware transistor selection (thin- and thick-oxide) in the IA enables a flexible tradeoff between noise and input impedance performances. The 2nd stage is a spike filter clocked by a parallel use of two non-overlapping clock generators, effectively tracking and suppressing the chopper spikes. The last stage is a gain-bandwidth-controllable amplifier for boosting the gain and alleviating different bio-potential signal measurements through simple digital controls. Simulation results showed that the RFE is capable of tolerating a differential electrode offset up to ±50mV, while achieving 140dB CMRR and 51.4nV/√Hz inputreferred noise density. The notch at 50Hz achieves 41dB rejection. The entire RFE consumes 16.55 to 35.5µA at 3V.

A 90nm CMOS bio-potential signal readout front-end with improved powerline interference rejection

This paper presents a real-time electrocardiogram (ECG) data compression processor with improved energy efficiency while maintaining high accuracy and real-time operation. Wavelet shrinkage is exploited to filter the noise and achieve sparse ECG signal representation. Adaptive temporal decimation is proposed to achieve configurable processing to adaptively reduce the data amount and computational activities for further power reduction. Modified Huffman and run-length wavelet source coding (MHRLC) is also designed to represent wavelet coefficients with optimized average code length and reduced memory requirement. Fabricated in 0.18- $\mu \text{m}$ CMOS, the ECG processor is implemented with customized near-threshold digital logics for minimum energy operation. The prototype was fully validated with the MIT-BIH Arrhythmia database. With a power consumption of 147–375 nW at 0.45 V, the proposed ECG processor exhibits a wide compression ratio ranging from 2.89 to 26.91, corresponding to a percentage-RMS-distortion from 0% to 3.11%.

A 0.45 V 147–375 nW ECG Compression Processor With Wavelet Shrinkage and Adaptive Temporal Decimation Architectures

Hilbert Huang Transform (HHT) is an empirical time-frequency analysis method firstly proposed by N. E. Huang in 1998. This method is suitable for nonlinear and non-stationary signal processing and thus employed for bioelectrical signal processing and analysis in recent years. However, the large computation cost of HHT restricts its applications for real-time signal processing. This paper presents a hardware accelerated HHT system based on Field Programmable Gate Array (FPGA), which employs hardware and software co-design techniques to effectively improve the processing speed.

Hardware-accelerated implementation of EMD

Existing research on human channel modeling of galvanic coupling intra-body communication (IBC) is primarily focused on the human body itself. Although galvanic coupling IBC is less disturbed by external influences during signal transmission, there are inevitable factors in real measurement scenarios such as the parasitic impedance of electrodes, impedance matching of the transceiver, etc. which might lead to deviations between the human model and the in vivo measurements. This paper proposes a field-circuit finite element method (FEM) model of galvanic coupling IBC in a real measurement environment to estimate the human channel gain. First an anisotropic concentric cylinder model of the electric field intra-body communication for human limbs was developed based on the galvanic method. Then the electric field model was combined with several impedance elements, which were equivalent in terms of parasitic impedance of the electrodes, input and output impedance of the transceiver, establishing a field-circuit FEM model. The results indicated that a circuit module equivalent to external factors can be added to the field-circuit model, which makes this model more complete, and the estimations based on the proposed field-circuit are in better agreement with the corresponding measurement results.

Mang I Vai

Papers

In-Fiber Mach–Zehnder Interferometer Sensor Based on Er Doped Fiber Peanut Structure in Fiber Ring Laser

A 90nm CMOS bio-potential signal readout front-end with improved powerline interference rejection

A 0.45 V 147–375 nW ECG Compression Processor With Wavelet Shrinkage and Adaptive Temporal Decimation Architectures

Hardware-accelerated implementation of EMD

A Novel Field-Circuit FEM Modeling and Channel Gain Estimation for Galvanic Coupling Real IBC Measurements.