I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next-generation wireless networks As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service requirements In this paper, we present a comprehensive literature review on the research progresses in wireless networks with RF energy harvesting capability, which is referred to as RF energy harvesting networks (RF-EHNs) First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques, and existing applications Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations and review the communication protocols specially designed for RF-EHNs We also explore various key design issues in the development of RF-EHNs according to the network types, ie, single-hop networks, multiantenna networks, relay networks, and cognitive radio networks Finally, we envision some open research directions

Wireless Networks With RF Energy Harvesting: A Contemporary Survey

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.

Wearable Sensors for Human Activity Monitoring: A Review

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

Wearable Sensors for Remote Health Monitoring.

We report a programmable analog bionic ear (cochlear implant) processor in a 1.5-/spl mu/m BiCMOS technology with a power consumption of 211 /spl mu/W and 77-dB dynamic range of operation. The 9.58 mm/spl times/9.23 mm processor chip runs on a 2.8 V supply and has a power consumption that is lower than state-of-the-art analog-to-digital (A/D)-then-DSP designs by a factor of 25. It is suitable for use in fully implanted cochlear-implant systems of the future which require decades of operation on a 100-mAh rechargeable battery with a finite number of charge-discharge cycles. It may also be used as an ultra-low-power spectrum-analysis front end in portable speech-recognition systems. The power consumption of the processor includes the 100 /spl mu/W power consumption of a JFET-buffered electret microphone and an associated on-chip microphone front end. An automatic gain control circuit compresses the 77-dB input dynamic range into a narrower internal dynamic range (IDR) of 57 dB at which each of the 16 spectral channels of the processor operate. The output bits of the processor are scanned and reported off chip in a format suitable for continuous-interleaved-sampling stimulation of electrodes. Power-supply-immune biasing circuits ensure robust operation of the processor in the high-RF-noise environment typical of cochlear implant systems.

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An ultra-low-power programmable analog bionic ear processor

Pulse oximeters are ubiquitous in modern medicine to noninvasively measure the percentage of oxygenated hemoglobin in a patient's blood by comparing the transmission characteristics of red and infrared light-emitting diode light through the patient's finger with a photoreceptor. We present an analog single-chip pulse oximeter with 4.8-mW total power dissipation, which is an order of magnitude below our measurements on commercial implementations. The majority of this power reduction is due to the use of a novel logarithmic transimpedance amplifier with inherent contrast sensitivity, distributed amplification, unilateralization, and automatic loop gain control. The transimpedance amplifier, together with a photodiode current source, form a high-performance photoreceptor with characteristics similar to those found in nature, which allows LED power to be reduced. Therefore, our oximeter is well suited for portable medical applications, such as continuous home-care monitoring for elderly or chronic patients, emergency patient transport, remote soldier monitoring, and wireless medical sensing. Furthermore, our design obviates the need for an A-to-D and digital signal processor and leads to a small single-chip solution. We outline how extensions of our work could lead to submilliwatt oximeters.

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An Ultra-Low-Power Pulse Oximeter Implemented With an Energy-Efficient Transimpedance Amplifier

A 75 dB 251 /spl mu/W analog speech processor is described that preserves the performance, robustness, and programmability needed for deaf patients at a reduced power consumption compared to that of implementations with A/D and DSP. It also provides zero-crossing outputs for stimulation strategies that use phase information to improve performance.

An analog bionic ear processor with zero-crossing detection

A visual prostheses codes visual signals into electrical stimulation patterns for the creation of artificial vision. In some examples, coding of the information uses image compression techniques, temporal coding strategies, continuous interleaved sampling (CIS), and/or radar or sonar data. Examples of the approach are not limited to processing visual signals but can also be used to processing signals at other frequency ranges (e.g., infrared, radio frequency, and ultrasound), for instance, creating an augmented visual sensation.

Coding for visual prostheses

This work presents a compressing-and-expanding, i.e., companding, strategy for spectral enhancement inspired by the operation of the auditory system. The companding strategy simulates the two-tone suppression phenomena of the auditory system and implements a soft local winner-take-all-like enhancement of the input spectrum. It performs multichannel syllabic compression without degrading spectral contrast. The companding strategy works in an analog fashion without explicit decision making, without the use of the fast Fourier transform, and without any cross-coupling between spectral channels. The strategy is useful in cochlear-implant processors, hearing aids, and speech recognition for enhancing spectral contrast. It is well suited to low-power analog circuit implementations.

Lorenzo Turicchia

Papers

An ultra-low-power programmable analog bionic ear processor

An Ultra-Low-Power Pulse Oximeter Implemented With an Energy-Efficient Transimpedance Amplifier

An analog bionic ear processor with zero-crossing detection

Coding for visual prostheses

A bio-inspired companding strategy for spectral enhancement