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

The aim of this article is to independently implement an indispensable front-end platform in a network-based intelligent home healthcare system. We propose to realize an ARM-cored structure embedded with μClinux system to integrate several kinds of medical measuring modules with our platform. Then we demonstrate the platform work cooperatively with all proposed and built -units. This most characteristic point of this platform lies in the implementation of the embedded expert system on the hardware basis. This kind of feature allows those aged and/or long-time patients to get medical diagnosis and advices at home, and, at the same time makes doctors work more effectively and pertinently.

A Front-end Platform of the Network-based Intelligent Home Healthcare Embedded System

Intra-body communication (IBC) is a technique to transmit electric signal through the on-body and implanted sensors by means of the conducting properties of the human body. This technique could be an innovative networking method for sensors on the human body and sharing information among the devices scattered across the human body. To analyze the characteristics of human data channel, we devise a quantitative measurement system that can provide a quantitative current in the transmitter and a qualitative analysis in the receiver. The transmitter of the system includes a current driving circuit with an amplifying and filtering circuit in receiver. By testing performance of the system and conducting in-vivo experiments with the proposed measurement system, the physical characteristic of human data channel was studied. Experimental results show that the human body has a stable propagation characteristic in the frequency range from 1 kHz to 100 kHz, and the potential gain characteristics approximate to a high-pass filter.

Measurement system with experiments for galvanic coupling type intra-body communication

Tilted fiber Bragg grating (TFBG) sensors are widely used in biochemical and electrochemical sensing due to their high sensitivity to refractive index (RI) of the surrounding medium. Ability of TFBGs to measure small RI change is currently limited due to the discrete spectral characteristics of TFBG cladding modes. Here we propose a method to measure both small and large RI changes with high accuracy and sensitivity by measuring the derivative of the intensity of the entire cladding mode envelope. The peak of the derivative curve provides a continuous and linear response to the RI change, which overcomes the problem of “cut-off mode hopping” that, until now, has limited the resolution of RI measurement using TFBGs. Experimental results demonstrate that, for small RI change (less than 0.002 RIU), the cladding envelope derivative method improves the interrogation sensitivity from tens of nm/RIU (using cut-off mode) to hundreds of nm/RIU (this method) together with a linear response (better than 98%). Moreover, such method provides the ability of temperature-crosstalk calibration, offering a powerful new tool for biochemical detection.

Improving Accuracy and Sensitivity of a Tilted Fiber Bragg Grating Refractometer Using Cladding Mode Envelope Derivative

This paper introduces a general purpose intelligent adaptive biosignal acquisition system both using Field Programmable Gate Array (FPGA) and Field Programmable Analog Array (FPAA). The design system inherits the powerful properties from the above devices to provide a stable and adaptive platform for processing various biosignals like ECG, EMG and EEG, etc. This system implementation solves the complexity in biosignal samples acquisition in home healthcare and stability in long term monitoring. It can be dynamically reconfigured and adapt to acquire different biosignals without changing any hardware. This system provides a simplified testing platform allowing different algorithms for processing real time biosignals embedded into it. In additions, the RS232 serial data port (or USB) can implement a connection with personal computer to ease and speed up the acquiring of biosignal samples or test results for further analysis. Alternatively, the system is also designed for outputting acquired and/or processed biosignals to PDA/PC at real time for visual inspection of results.

General Purpose Adaptive Biosignal Acquisition System Combining FPGA and FPAA

In recent electroencephalograph (EEG)-based emotion recognition, the differential entropy (DE) features extracted from multiple electrodes are organized as a 2D feature map for convolutional neural network (CNN) in order to utilize the information hidden in the electrodes. In this study, we attempt to investigate the influence of different feature maps on the recognition performance. Six different 2D feature maps (M1-M4: baseline feature maps without sparsity and location relationship, M5-M6: pre-defined feature maps with sparsity and location relationship) are used to organize the DE features for the traditional CNN model. Evaluation study on the DEAP dataset finds that the 2D feature map configuration exhibits statistically significant effect on the classification performance of the traditional CNN model in classifying the high/low arousal and high/low valence, respectively. However, the differences are rather limited, e.g., only 1% improvement can be resulted from selecting the optimal 2D feature map among 6 feature maps. This implies that the feature map may not be a critical issue when applying the DE features to classifying the emotion states in a CNN.

Mang I Vai

Papers

A Front-end Platform of the Network-based Intelligent Home Healthcare Embedded System

Measurement system with experiments for galvanic coupling type intra-body communication

Improving Accuracy and Sensitivity of a Tilted Fiber Bragg Grating Refractometer Using Cladding Mode Envelope Derivative

General Purpose Adaptive Biosignal Acquisition System Combining FPGA and FPAA

EEG-based Emotion Recognition Under Convolutional Neural Network with Differential Entropy Feature Maps