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

This paper presents a novel method based on deterministic annealing to circumvent the problem of the sensitivity to atypical observations associated with the maximum likelihood (ML) estimator via conventional EM algorithm for mixture models. In order to learn the mixture models in a robust way, the parameters of mixture model are estimated by trimmed likelihood estimator (TLE), and the learning process is controlled by temperature based on the principle of maximum entropy. Moreover, we apply the proposed method to the single-trial electroencephalography (EEG) classification task. The motivation of this work is to eliminate the negative effects of artifacts in EEG data, which usually exist in real-life environments, and the experimental results demonstrate that the proposed method can successfully detect the outliers and therefore achieve more reliable result.

Robust learning of mixture models and its application on trial pruning for EEG signal analysis

Intra-Body Communication (IBC), which employs the human body as the channel to transmit health informatics, is a potential approach for connecting wearable and implantable devices. Undoubtedly, its channel characteristics provide deep insights to select parameters that are beneficial for the physical layer design of the Body Area Network (BAN). Nevertheless, such analysis has not been systematically addressed. To this end, the transfer function from the quasi-static model in the electromagnetic field is considered at first. Then, the linearity of the galvanic coupling IBC channel is verified. Besides, the proper frequency band for data transmission is investigated using the water-filling algorithm. Moreover, the theoretical Bit Error Rate (BER) performances of different modulations are studied. Results revealed that the galvanic coupling IBC channel can be modeled as a linear band-limited Additive White Gaussian Noise (AWGN) channel. Finally, suitable modulations for various scenarios are discussed based on the channel characteristics found. Consequently, this work can further enrich the design of the IBC for medical applications.

Channel Characteristics Analysis of Galvanic Coupling Intra-Body Communication

Synthetic aperture (SA) imaging algorithm, which combines sub-aperture elements to form high resolution image, can improve image quality in vivo ultrasound image. However, large computation resource is generally required for the implementation of SA. In this paper, we propose a parallel structural design for SA imaging algorithm that can be realized in Field Programmable Gate Array (FPGA), so that light weight and miniaturized design can be achieved. The proposed design has been validated by MATLAB and was employed to construct a high resolution ultrasound imaging from the raw data of 128 array transducer elements. The processing time is about 2.6ms theoretically, which should be alright for usage.

Parallel design for ultrasound synthetic aperture imaging FPGA

An embossed capacitive micromachined ultrasonic transducer (CMUT) is a device with embossed membrane that works in the collapse mode to improve output pressure in transmission. In this paper, a six-mask sacrificial release process is proposed for fabricating embossed CMUT arrays. Based on this process, the embossed pattern CMUTs were firstly fabricated. By using of electroplating methods, annular embossed patterns made of nickel were grown on the full top electrodes of CMUTs. The dimensions of the embossed pattern were about 3.0 μm in width and 1.4 μm in height. The resonant frequencies of the embossed CMUT array were 6.4MHz and 8.7MHz when the device worked in the conventional and the collapse mode, respectively.

Fabrication of embossed capacitive micromachined ultrasonic transducers using sacrificial release process

The galvanic coupling intra-body communication (IBC) is a novel short distance information transmission method for Body Area Networks. It uses human body as the transmission media to realize data transmission and sharing among all the electronic devices scattered on, in or around the human body. It is especially suitable for information interactions for long term and continuous medical signal detections for human bodies for wearable/implanted medical devices.Based on the principle analysis of the galvanic coupling IBC previously, this paper attempted to design a highly reliable and stable transceiver. The transmitter modulated the base band signal with 2CPFSK (2 continuous phase FSK) and converted it into alternating current signal suitable for human body transmission. The receiver collected the coupling voltage signal differentially, and then demodulated it. Finally, the transceiver completed the data transmission in the case of signal person and many people with finger-touching. It provided a successful experience for the development and practical application of IBC and Body Area Networks.

Mang I Vai

Papers

Robust learning of mixture models and its application on trial pruning for EEG signal analysis

Channel Characteristics Analysis of Galvanic Coupling Intra-Body Communication

Parallel design for ultrasound synthetic aperture imaging FPGA

Fabrication of embossed capacitive micromachined ultrasonic transducers using sacrificial release process

A transceiver designed for intra-body communication of body area networks