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

On robust estimation of the location parameter

The organization of behavior: A neuropsychological theory

Power Aware Routing in Mobile Ad Hoc Networks

Deep learning in spiking neural networks

Geographic routing offers a radical departure from previous topology-dependent routing paradigms through its use of physical location in the routing process. Geographic routing protocols eliminate dependence on topology storage and the associated costs, which also makes them more suitable to handling dynamic behavior frequently found in wireless ad-hoc networks. Geographic routing protocols have been designed for a variety of applications ranging from mobility prediction and management through to anonymous routing and from energy efficiency to QoS. Geographic routing is also part of the larger area of context-awareness due to its usage of location data to make routing decisions and thus represents an important step in the journey towards ubiquitous computing. The focus of this paper, within the area of geographic routing is on wireless ad-hoc networks and how location information can benefit routing. This paper aims to provide both a comprehensive and methodical survey of existing literature in the area of geographic routing from its inception as well as acting as an introduction to the subject.

A Survey of Geographical Routing in Wireless Ad-Hoc Networks

The development of real-time locating systems (RTLS) has become an important add-on to many existing location aware systems. While GPS has solved most of the outdoor RTLS problems, it fails to repeat this success indoors. A number of technologies have been used to address the indoor tracking problem. The ability to accurately track the location of people indoors has many applications ranging from medical, military and logistical to entertainment. However, current systems cannot provide continuous real-time tracking of a moving target or lose capability when coverage is poor. The deployment of a real-time location determination system however is fraught with problems. To date there has been little research into comparing commercial systems on the market with regard to informing IT departments as to their performance in various aspects which are important to tracking devices and people in relatively confined areas. This article attempts to provide such a useful comparison by providing a review of the practicalities of installing certain location-sensing systems. We also comment on the accuracies achieved and problems encountered using the position-sensing systems.

An evaluation of indoor location determination technologies

This paper presents a synaptic weight association training (SWAT) algorithm for spiking neural networks (SNNs). SWAT merges the Bienenstock-Cooper-Munro (BCM) learning rule with spike timing dependent plasticity (STDP). The STDP/BCM rule yields a unimodal weight distribution where the height of the plasticity window associated with STDP is modulated causing stability after a period of training. The SNN uses a single training neuron in the training phase where data associated with all classes is passed to this neuron. The rule then maps weights to the classifying output neurons to reflect similarities in the data across the classes. The SNN also includes both excitatory and inhibitory facilitating synapses which create a frequency routing capability allowing the information presented to the network to be routed to different hidden layer neurons. A variable neuron threshold level simulates the refractory period. SWAT is initially benchmarked against the nonlinearly separable Iris and Wisconsin Breast Cancer datasets. Results presented show that the proposed training algorithm exhibits a convergence accuracy of 95.5% and 96.2% for the Iris and Wisconsin training sets, respectively, and 95.3% and 96.7% for the testing sets, noise experiments show that SWAT has a good generalization capability. SWAT is also benchmarked using an isolated digit automatic speech recognition (ASR) system where a subset of the TI46 speech corpus is used. Results show that with SWAT as the classifier, the ASR system provides an accuracy of 98.875% for training and 95.25% for testing.

/pdf/swat-a-spiking-neural-network-training-algorithm-for-1s64xq2qs6.pdf

SWAT: A Spiking Neural Network Training Algorithm for Classification Problems

Device mobility is an issue that affects both Mobile ad hoc networks (MANETs) and opportunistic networks. While the former employs conventional routing techniques with some element of mobility management, opportunistic networking protocols often use mobility as a means of delivering messages in intermittently connected networks. If nodes are able to determine the future locations of other nodes with reasonable accuracy then they could plan ahead and take into account and even benefit from such mobility. In an ad hoc network, devices form a network amongst themselves and forward packets for each other without infrastructure. Ad hoc networks could be deployed in a disaster scenario to enable communications between responders and base camp to provide telemedicine services. However, most ad hoc routing protocols cannot meet the necessary standards for streaming multimedia because they do not attempt to manage quality of service (QoS). Node mobility adds an additional layer of complexity leading to potentially detrimental effects on QoS. Geographic routing protocols use physical locations to make routing decisions and are typically lightweight, distributed, and require only local network knowledge. They are thus less susceptible to the effects of mobility, but are not impervious. Location-prediction can be used to enhance geographic routing, and counter the negative effects of mobility, but this has received relatively little attention. Location prediction in combination with geographic routing has been explored in previous literature. Most of these location prediction schemes have made simplistic assumptions about mobility. However more advanced location prediction schemes using machine learning techniques have been used for wireless infrastructure networks. These approaches rely on the use of infrastructure and are therefore unsuitable for use in opportunistic networks or MANETs. To solve the problem of accurately predicting future location in non-infrastructure networks, we investigate the prediction of continuous numerical coordinates using artificial neural networks. Simulation using three different mobility models representing human mobility has shown an average prediction error of <1 m in normal circumstances.

/pdf/location-and-mobility-aware-routing-for-improving-multimedia-w9zzpcomna.pdf

Jose Santos

Papers

A Survey of Geographical Routing in Wireless Ad-Hoc Networks

An evaluation of indoor location determination technologies

SWAT: A Spiking Neural Network Training Algorithm for Classification Problems

Location and Mobility-Aware Routing for Improving Multimedia Streaming Performance in MANETs

Novel sparse LSSVR models in primal weight space for robust system identification with outliers