An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of Automatic Modulation Classification (AMC) which deals with modulation classification was a pattern recognition problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude Modulation (M-QAM) which underneath different channel scenarios was well detailed. A search of the literature revealed indicates that few studies were done on the classification of high order M-QAM modulation schemes like128-QAM, 256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting Higher-Order Cumulant's (HOC) features from input data received raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN) channel with four effective parameters which were defined to distinguished the types of modulation from the set; 4-QAM~1024-QAM. This approach makes the recognizer more intelligent and improves the success rate of classification. From simulation results, which was achieved under statistical models for noisy channels, manifest that recognized algorithm executes was recognizing in M-QAM, furthermore, most results were promising and showed that the logarithmic classifier works well over both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero), it can be considered as an Integrated Automatic Modulation Classification (AMC) system in order to identify high order of M-QAM signals that applied a unique logarithmic classifier, to represents higher versatility, hence it has a superior performance via all previous works in automatic modulation identification systemComment: 18 page

Representation of acoustic communication signals by insect auditory receptor neurons

Despite their simple auditory systems, some insect species recognize certain temporal aspects of acoustic stimuli with an acuity equal to that of vertebrates; however, the underlying neural mechanisms and coding schemes are only partially understood. In this study, we analyze the response characteristics of the peripheral auditory system of grasshoppers with special emphasis on the representation of species-specific communication signals. We use both natural calling songs and artificial random stimuli designed to focus on two low-order statistical properties of the songs: their typical time scales and the distribution of their modulation amplitudes. Based on stimulus reconstruction techniques and quantified within an information-theoretic framework, our data show that artificial stimuli with typical time scales of >40 msec can be read from single spike trains with high accuracy. Faster stimulus variations can be reconstructed only for behaviorally relevant amplitude distributions. The highest rates of information transmission (180 bits/sec) and the highest coding efficiencies (40%) are obtained for stimuli that capture both the time scales and amplitude distributions of natural songs. Use of multiple spike trains significantly improves the reconstruction of stimuli that vary on time scales <40 msec or feature amplitude distributions as occur when several grasshopper songs overlap. Signal-to-noise ratios obtained from the reconstructions of natural songs do not exceed those obtained from artificial stimuli with the same low-order statistical properties. We conclude that auditory receptor neurons are optimized to extract both the time scales and the amplitude distribution of natural songs. They are not optimized, however, to extract higher-order statistical properties of the song-specific rhythmic patterns

Artefact reduction in photoplethysmography

The use of optical techniques in biomedical monitoring and diagnosis is becoming increasingly widespread, primarily because of the non-invasive nature of optically derived measurements. Physiological analysis is usually achieved by characterisation of the spectral or temporal properties of the interaction between light and the anatomy. Although some optical measurements require complex instrumentation and protocols, recent technological advances have resulted in robust and compact equipment that is now used routinely in a multitude of clinical contexts. Unfortunately, these measurements are inherently sensitive to corruption from dynamic physical conditions or external sources of light, inducing signal artefact. Artefact is the primary restriction in the applicability of many optical measurements, especially for ambulatory monitoring and tele-medicine. The most widely used optical measurement is photoplethysmography, a technique that registers dynamic changes in blood volume throughout the peripheral vasculature and can be used to screen for a number of venous disorders, as well as monitoring the cardio-vascular pulse wave. Although photoplethysmographic devices are now incorporated into many patient-monitoring systems, the prevalent application is a measurement known as pulse oximetry, which utilises spectral analysis of the peripheral blood to estimate the arterial haernoglobin oxygen saturation. Pulse oximetry is well established as an early warning for hypoxia and is now mandatory under anaesthesia in many countries. The problem of artefact is prominent in these continuous monitoring techniques, where it is often impossible to control the physical conditions during use. This thesis investigates the possibility of reducing artefact corruption of photoplethysmographic signals in real time, using an electronic processing methodology that is based upon inversion of a physical artefact model. The consequences of this non-linear artefact reduction technique for subsequent signal analysis are discussed, culminating in a modified formulation for pulse oximetry that not only has reduced sensitivity to artefact but also possesses increased generality. The design and construction of a practical electronic system is then used to explore both the implementation issues and the scope of this technique. The performance of artefact reduction obtained is then quantified under realistic experimental conditions, demonstrating that this methodology is successful in removing or reducing a large proportion of artefact encountered in clinically relevant situations. It is concluded that non-linear artefact reduction can be applied to any photoplethysmographic technology, reducing interpretation inaccuracies that would otherwise be induced by signal artefact. It is also speculated that this technology could enable the use of photoplethysmographic systems in applications that are currently precluded by the inherent severity of artefact

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

A general theory of phase noise in electrical oscillators

A general model is introduced which is capable of making accurate, quantitative predictions about the phase noise of different types of electrical oscillators by acknowledging the true periodically time-varying nature of all oscillators. This new approach also elucidates several previously unknown design criteria for reducing close-in phase noise by identifying the mechanisms by which intrinsic device noise and external noise sources contribute to the total phase noise. In particular, it explains the details of how 1/f noise in a device upconverts into close-in phase noise and identifies methods to suppress this upconversion. The theory also naturally accommodates cyclostationary noise sources, leading to additional important design insights. The model reduces to previously available phase noise models as special cases. Excellent agreement among theory, simulations, and measurements is observed

Requirements Study for System Implementation of an Atmospheric Laser Propagation Experiment Program, Volume II

Program planning, ground support and airborne equipment for laser space communication syste