John R. Terry

(NOTE: Each chapter concludes with a Bibliography.) Preface. 1. Background and WLAN Overview. Review of Stochastic Processes and Random Variables. Review of Discrete-Time Signal Processing. Components of a Digital Communication System. OFDM WLAN Overview. Single Carrier Versus OFDM Comparison. 2. Synchronization. Timing Estimation. Frequency Synchronization. Channel Estimation. Clear Channel Assessment. Signal Quality. 3. Modulation and Coding. Modulation. Interleaving. Channel Codes. 4. Antenna Diversity. Background. Receive Diversity. Transmit Diversity. 5. RF Distortion Analysis for OFDM WLAN. Components of the Radio Frequency Subsystem. Predistortion Techniques for Nonlinear Distortion Mitigation. Adaptive Predistortion Techniques. Coding Techniques for Amplifier Nonlinear Distortion Mitigation. Phase Noise. IQ Imbalance. 6. Medium Access Control (MAC)for IEEE 802.ll Networks. MAC Overview. MAC System Architecture. MAC Frame Formats. MAC Data Services. MAC Management Services. MAC Management Information Base. 7. Medium Access Control (MAC) for HiperLAN/2 Networks. Network Architecture. DLC Functions. MAC Overview. Basic MAC Message Formats. PDU Trains. MAC Frame Structure. Building a MAC Frame. MAC Frame Processing. 8. Rapid Prototyping for WLANs. Introduction to Rapid Prototype Design. Good Digital Design Practices. Rapid Prototyping of a WLAN System. Index.

The aim of this paper is to explain critical features of the human primary generalized epilepsies by investigating the dynamical bifurcations of a nonlinear model of the brain's mean field dynamics. The model treats the cortex as a medium for the propagation of waves of electrical activity, incorporating key physiological processes such as propagation delays, membrane physiology, and corticothalamic feedback. Previous analyses have demonstrated its descriptive validity in a wide range of healthy states and yielded specific predictions with regards to seizure phenomena. We show that mapping the structure of the nonlinear bifurcation set predicts a number of crucial dynamic processes, including the onset of periodic and chaotic dynamics as well as multistability. Quantitative study of electrophysiological data supports the validity of these predictions. Hence, we argue that the core electrophysiological and cognitive differences between tonic-clonic and absence seizures are predicted and interrelated by the global bifurcation diagram of the model's dynamics. The present study is the first to present a unifying explanation of these generalized seizures using the bifurcation analysis of a dynamical model of the brain.

The advance of Parkinson's disease is associated with the existence of abnormal oscillations within the basal ganglia with frequencies in the beta band (13–30 Hz). While the origin of these oscillations remains unknown, there is some evidence suggesting that oscillations observed in the basal ganglia arise due to interactions of two nuclei: the subthalamic nucleus (STN) and the globus pallidus pars externa (GPe). To investigate this hypothesis, we develop a computational model of the STN–GPe network based upon anatomical and electrophysiological studies. Significantly, our study shows that for certain parameter regimes, the model intrinsically oscillates in the beta range. Through an analytical study of the model, we identify a simple set of necessary conditions on model parameters that guarantees the existence of beta oscillations. These conditions for generation of oscillations are described by a set of simple inequalities and can be summarized as follows: (1) The excitatory connections from STN to GPe and the inhibitory connections from GPe to STN need to be sufficiently strong. (2) The time required by neurons to react to their inputs needs to be short relative to synaptic transmission delays. (3) The excitatory input from the cortex to STN needs to be high relative to the inhibition from striatum to GPe. We confirmed the validity of these conditions via numerical simulation. These conditions describe changes in parameters that are consistent with those expected as a result of the development of Parkinson's disease, and predict manipulations that could inhibit the pathological oscillations.

The hypothalamic-pituitary-adrenal (HPA) axis regulates circulating levels of glucocorticoid hormones, and is the major neuroendocrine system in mammals that provides a rapid response and defense against stress. Under basal (i.e., unstressed) conditions, glucocorticoids are released with a pronounced circadian rhythm, characterized by peak levels of glucocorticoids during the active phase, that is daytime in humans and nighttime in nocturnal animals such as mice and rats. When studied in more detail, it becomes clear that the circadian rhythm of the HPA axis is characterized by a pulsatile release of glucocorticoids from the adrenal gland that results in rapid ultradian oscillations of hormone levels both in the blood and within target tissues, including the brain. In this review, we discuss the regulation of these circadian and ultradian HPA rhythms, how these rhythms change in health and disease, and how they affect the physiology and behavior of the organism.

The hypothalamic-pituitary-adrenal (HPA) axis is a neuroendocrine system that regulates the circulating levels of vital glucocorticoid hormones. The activity of the HPA axis is characterized not only by a classic circadian rhythm, but also by an ultradian pattern of discrete pulsatile release of glucocorticoids. A number of psychiatric and metabolic diseases are associated with changes in glucocorticoid pulsatility, and it is now clear that glucocorticoid responsive genes respond to these rapid fluctuations in a biologically meaningful way. Theoretical modelling has enabled us to identify and explore potential mechanisms underlying the ultradian activity in this axis, which to date have not been identified successfully. We demonstrate that the combination of delay with feed-forward and feedback loops in the pituitary-adrenal system is sufficient to give rise to ultradian pulsatility in the absence of an ultradian source from a supra-pituitary site. Moreover, our model enables us to predict the different patterns of glucocorticoid release mediated by changes in hypophysial-portal corticotrophin-releasing hormone levels, with results that parallel our experimental in vivo data.